Search

In order to effectively improve the capability of shore-to-ship missile penetration, unmanned aerial vehicles (UAV) were used for distributed combat against enemy shipborne radar. Based on radar detection equation and interference equation, the calculation model of radar exposure area under UAV interference was established, and the configuration of UAV array was analyzed. The results show that the jamming direction, jamming power and jamming distance of UAV have important effects on the detection performance of shipborne radar. The distributed operation of UAV can effectively reduce the combat effectiveness of radar, thus providing favorable conditions for shore-to-ship missile penetration, and laying an important theoretical foundation for future research on the tactical application of UAV as a new combat style. [ABSTRACT FROM AUTHOR]

Objectives: The ground-based radar has features such as working all-time in all-weather, providing continuous observations with high spatiotemporal resolutions and can monitor landslides with high precision in near real-time. However, atmospheric effects represent one major limitation of ground-based radar, due to the spatiotemporal variations of the troposphere, especially in mountainous areas. Atmospheric variations are often treated as static signals and corrected by the range function model which can be unreliable, particularly when strong dynamic atmospheric turbulence occurs. Methods: Based on its successful experience in the generic atmospheric correction online service for interferometric synthetic aperture radar (InSAR), the ground based iterative tropospheric decomposition (GBITD) model is utilized to decompose the tropospheric delay into stratification and turbulence components, and perform atmospheric correction. The GBITD model doesn't require global navigation satellite system or any external meteorological observations, which makes it flexible for InSAR atmospheric correction. Results: The application of the GBITD model to the 2018 Baige landslide suggests that the root mean square of two interferograms with temporal baselines of 2 min and 10 min decreased from 1.43 mm and 1.69 mm to 0.21 mm and 0.24 mm, respectively. The maximum cumulative displacement derived from the 778 images acquired during the period from 4 to 10 December 2018 was 1.4 m with the maximum displacement rate of 0.23 m/d. Conclusions: The experimental results show that the GBITD method performes best compared against the homogeneous model and the stratified model in complex mountainous areas. Furthermore, Ground-based radar has obvious unique advantages in landslide monitoring, which can provide strong technical support for landslide emergency monitoring and risk assessment. [ABSTRACT FROM AUTHOR]

Aiming at the high computational cost of traditional compressed sensing algorithms, this paper proposed a sparse imaging algorithm based on depth expansion model from the perspective of sparse signal recovery. Firstly, it constructed a complex sparse reconstruction network VAMP-Net. In VAMP-Net, it divided complex regressive echo signal into real part and imaginary part as input. Secondly, it substituted the input into the iterative block based on VAMP algorithm. Finally, it carried out the optimal nonlinear sparse transformation by convolutional neural module to obtain the recovered real part and imaginary part signals, and then merged them to obtain the restored target image. As for the proposed algorithm, this paper used artificial data sets to conduct simulation experiments under different target density, iteration times and noise environment, and compared with the traditional iterative shrinkage threshold algorithm and deep learning reconstruction algorithm. Then it used the measured data with different sparsity for field measurement verification. Experimental results show that the image reconstructed by this algorithm has better performance in NMSE, TBR, reconstruction speed and memory usage. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of precise classification of vegetation points and ground points using LiDAR point cloud data in dense forest terrain, five traditional filtering methods were used to evaluate the filtering accuracy of forest area point cloud, and the corresponding initial filtering was carried out with the help of cloth simulation filtering algorithm. After accurate processing, the initial ground points with relatively complete terrain features were obtained. Then, the iterative opening calculation was carried out on the initial ground points with prominent vegetation points, and the new ground points were obtained. And with the aid of a series of indicators such as Class Ⅰ error and Class Ⅱ error, the accuracy of scientific evaluation was determined. Compared with the traditional filtering algorithm of Class Ⅱ, the total error of the cloth simulation algorithm and simple morphology algorithm decreased significantly, and the Kappa coefficient showed an increasing trend. The results showed that this method was suitable for point cloud classification in forest area and can obtain better filtering effect. [ABSTRACT FROM AUTHOR]

The large-thickness concrete cover was detected by electromagnetic method and radar method, and the data were compared and analyzed with that obtained from direct method. The results indicate that the radar method is more accurate to detect the concrete cover, and it is used to detect the large -thickness concrete cover. The relative dielectric constant of concrete at different age was determined by making the calibration plate of concrete cover. The large-thickness concrete cover could be accurately detected by using the relative dielectric constant of concrete at 14 d age, which provide the basis for the optimization adjustment of steel binding construction and ensure the construction quality of reinforced concrete. [ABSTRACT FROM AUTHOR]

Field roads have been the most important transportation ways for commercial grain and agricultural products in hilly and mountainous regions in China. Especially, the youth labor can rapidly migrate from the rural to the urban areas against everincreasing urbanization. Current manual production cannot fully meet the large-scale and precision agriculture in recent years, owing to the protracted yield cycle and low rate of return. Therefore, it is urgent to implement the mechanization and intelligent agriculture for the national food security in hilly and mountainous regions. Among them, the autonomous and safe operation of intelligent machinery can be critical to navigate or circumvent obstacles on field roads. However, the conventional machine vision cannot accurately and rapidly construct the visual guidance lines in such terrains. In this study, the extraction was proposed to enhance the visual guidance and obstacle avoidance using millimeter-wave radar and vision fusion. The state information was also detected from the target objects on the field road. The specific steps were as follow. Preprocessing techniques were employed to filter the portion of the radar object data. A multi-target tracking was utilized to eliminate the interference data for the continuous tracking of dynamic objects. Accurate radar object data was then obtained for subsequent data fusion. A semantic segmentation network was created using Deeplabv3+, and then leveraged a dataset of the adjacent field roads. The millimeter-wave radar and vision data were synchronized in both time and space via the timestamp alignment and least squares-based coordinate transformation. Extraction approach was then established for the visual guidance lines, particularly for the scenarios where dynamic target state was both available and unavailable. A series of experiments were carried out to validate the extraction of visual guide line. The average errors of detection were ranged from 1.60 to 9.20 pixels at the real road midpoints in the scenes without dynamic targets. Moreover, the visual guidance lines were successfully extracted for the obstacle avoidance proactively when encountering dynamic targets. Evidently, the integrated approach was effectively overcome the constraints of traditional machine vision, thereby enhancing the safety and reliability of machinery operation in hilly and mountainous terrains. Meanwhile, the efficacy was depended mainly on the precise projection of millimeter-wave radar data onto the visual plane. Simultaneously, the variables were markedly introduced the positional discrepancies, such as the posture of platform (including pitch and roll). Furthermore, the driveability of the operational area was effectively evaluated in the forthcoming regions. At the same time, the real-world conditions were more complex in the frequent presence of common elements like livestock, vehicles, and tricycles. The resilience of approach can be expected to improve the radar data processing for the detection of static objects. Moreover, positional deviations can also be mitigated from mechanical movements. As such, the precise visual guidance lines were established in the hilly and mountainous terrains. Therefore, the radar data processing can be focused on the dataset expanding and position deviation that induced by various factors. The intelligent level of agricultural machinery can be improved in hilly and mountainous area. The finding is of great significance to consolidate national food security. [ABSTRACT FROM AUTHOR]

In order to improve the operational effectiveness of multi-sensor cooperative detection of carrier-bome fighter, based on the scene of air interception, according to the combat airspace from far to near, the scene model of the extended range search of carrier-borne fighter, the radar infrared fusion detection, the cooperative guidance of formation and radar multi-beam detection are established. In the over-the-horizon, medium-range and short-range search phase, the extended range detection, interference and multi-beam detection in the multi-sensor cooperative detection mode are simulated and analyzed. According to the different combat distance, the design of multi-sensor cooperative detection mode under the combat flow is innovatively carried out. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization, the intelligent transportation has received more and more attention. Among them, the use of depth completion technology to extract the depth information of objects plays an important role in the realization of vehicle target tracking, distance calculation between targets and other tasks. However, multi-source depth completion data collected in practice often have correlation bias, resulting in knotty errors. In this regard, this study studies the depth completion technology based on multi-source data association fusion. The proposed technology enhances the depth map by calculating multi-channel confidence, and performs more accurate point-by-point correlation between the image and the millimeter-wave radar point cloud data. By designing a multi-scale attention fusion module, the adaptive fusion of multi-granularity associated data is realized to generate high-quality depth maps. In this study, a large number of experiments have been carried out in the public nuScenes data set. The experimental results show that the mean absolute error of our method is 1.142 m, which is lower than the 1.472 m of the existing benchmark method. [ABSTRACT FROM AUTHOR]

To explore the applicability of deep learning methods to radar rainfall nowcasting and flood forecasting, U-Net, Attention-Unet and TransAtt-Unet are used to carry out rainfall nowcasting. The nowcasted rainfall results are used as inputs to the HEC-HMS hydrological model for flood forecasting. The results show that with a 1-hour lead time, Attention-Unet has the best performance in nowcasting heavy rainfall with a short duration, and the relative errors in the simulated flood peak and runoff volume by the nowcasted rainfall of TransAtt-Unet are less than 20%. Each deep learning model has a good forecasting accuracy for rainfall and flood events with large magnitudes. The rainfall intensity, rainfall totals, flood peaks and runoff volumes are significantly underestimated with a 2-hour lead time, with U-Net achieving relatively good rainfall nowcasting. The 1-hour lead time radar rainfall nowcasting and flood forecasting based on deep learning can provide a scientific reference for watershed flood prevention and mitigation. [ABSTRACT FROM AUTHOR]

Objectives: Millimeter wave radar has been widely used in automotive industry and other fields, but its application is mainly limited to the environmental perception of obstacles or specific tasks. At present, there is little research on the application of millimeter wave radar in the field of navigation and positioning. Methods: This paper first studies the raw data processing principle of millimeter wave radar, and then designs an indoor ego localization method which only depends on a low‐cost millimeter wave radar. The process mainly includes extracting centroid feature points using density based spatial clustering of applications with noise (DBSCAN) algorithm, matching centroid feature point pairs through nearest neighbor criterion, constructing nonlinear optimization function and solving positioning results using levenberg marquardt method. Results and Conclusions: Experiments show that indoor navigation and positioning can be solved in real time by using a low‐cost millimeter wave radar. Under static conditions, the average horizontal positioning accuracy can reach sub centimeter level (mean value is 0.82 cm and standard deviation is 0.47 cm). Under dynamic conditions, the absolute trajectory error can reach 0.66 m and the average heading angle error can reach 4.58°, which shows the feasibility of ego‐localization of low‐cost millimeter wave radar. Finally, this paper discusses the problems and feasible research ideas of low cost millimeter wave radar in navigation and positioning. [ABSTRACT FROM AUTHOR]

To investigate the automatic monitoring technology of key construction nodes and structures of super-large bridges constructed with cantilever, the 0# block radar measurement and control, the settlement monitoring of construction sections and the mechanical response monitoring of whole bridge were carried out during the deformation and stress stages of the cantilever construction bridge section. The measurement and control data of stress and deformation in actual construction and simulation data were compared and analyzed, and the safety state of bridge section during construction was judged by the coincidence. By the mathematical statistics method, the accuracy of radar monitoring data was compared with that of traditional monitoring data. The research results show that radar monitoring can work around the clock and can analyze typical fluctuations in cloud images in combination with construction logs. The fitting value of millimeter-wave radar monitoring block 0# deformation data is 0.051 44, which is better than 0.006 32 of the traditional monitoring method and has stronger correlation with the numerical simulation curve. During the whole construction period, the monitoring data of the force and deformation of each segment of the whole bridge fluctuate around the theoretical values with small fluctuation range, and the continuous beam is always in safe state. [ABSTRACT FROM AUTHOR]

Non-destructive testing of tree roots has the vast application potential to the root system evaluation of fruit and ancient trees for the better management of plant health. Among them, ground-penetrating radar (GPR) can be expected to nondestructively detect the tree roots, due to the portable and low-cost. However, the interpretation of GPR data is dependent mainly on the manual analysis, leading to the reduced automation and accuracy. Moreover, the clutters and noise that caused by soil heterogeneity can severely affect the accuracy of root detection and identification. In this study, a detection method was proposed for the clutter suppression and root parameter prediction of ground penetrating radar. The data collection was divided into two parts: the dataset and field test. The datasets consisted of simulated and synthesized real data. The roots with the radius ranging from 5 to 30 mm were randomly distributed within 300 mm underground. The field experiment was conducted in three sand pits. The soil heterogeneity was simulated to create the different moisture levels, when adding water into different parts in two of the dry sand pits. The rest sandpit was a condition of higher water content after heavy rainfall. At the same time, the roots were excavated to verify the model under real soil conditions. Four test pits contained the roots of different tree species, the different soil environments, as well as the different radii and depths. The attention mechanism was integrated into the U-net model, in order to enhance the capability in the clutter suppression and root target reflection restoration. A clutter suppression network was constructed to test the effectiveness of the model. The encoding-decoding structure of U-net was separated the target root hyperbolic reflection from the overall background. The attention mechanism enabled the network to better adaptively focus on the hyperbolic reflection of the target root system without identifying the clutter as the target reflection. The clutter suppression was realized for the original B-scan image to remove the adverse effects, due to the soil heterogeneity and radar antenna coupling. Furthermore, the network of root parameter prediction was constructed using residual blocks and inception. The multi-scale receptive field of inception was used to extract the global and local features, and simultaneously to predict the root radius and depth. The predicted results were obtained by parallel inputting the clutter suppressed image and the original B-scan image into the network. The effectiveness of the model was evaluated using datasets and field experiments. The test results of clutter suppression were evaluated using peak signal to noise ratio (PSNR) and structural similarity (SSIM), which were 39.42 dB and 0.991, respectively, better than before. The test results on datasets showed the mean absolute error (MAE) of 1.7 mm, and the coefficient of determination R² value of 0.914 for the root radius prediction, while the mean absolute error (MAE) of 6.3 mm and the R² value of 0.989 for the root depth prediction. The clutter suppression was also achieved in the better performance than the rest on the field data. The maximum prediction errors for the root radius and depth were 1.85, and 13.6 mm, respectively, where the total average relative error was 6.55%. Influencing factors were determined for the prediction effectiveness of ground-penetrating radar. The future research directions were pointed out as well. The findings can be applied to the actual prediction of root depth and radius of tree roots, particularly for the health administration of fruit trees and the protection of ancient trees.protection of ancient trees.protection of ancient trees. [ABSTRACT FROM AUTHOR]

In order to solve the problems of poor anti-interference and invasion of user privacy in traditional methods of using cameras for human activity recognition, this paper proposed a human activity recognition network based on spatiotemporal attention of millimeter wave radar 3D point cloud data to achieve accurate perception of intelligent application context. The network firstly used a secondary sliding time window to accumulate and separate the point cloud data generated by human activities as the input of the classifier, then used the PointLSTM unit to aggregate point features and states according to the point cloud coordinate relationship to extract the time sequence features of human activities, and then spliced temporal-spatial features, reduced the overall network computation and enhanced the network’s aggregation ability for local featured through sampling grouping modules, and finally used a stacked attention module to deeply fuse global and local features in temporal-spatial point cloud data to complete the accurate classification of human activities. This paper used millimeter wave radar to collect point cloud datasets of various human activities, the experimental results show that the average accuracy of the proposed spatiotemporal attention network can reach 98.64%,which can effectively identify complex and small-difference human activities, and meet the requirements of the human activity recognition system. [ABSTRACT FROM AUTHOR]

In order to study the waveform selection of radar seeker in the terminal guidance and tracking phase, this paper proposes a waveform selection algorithm in the guidance and tracking process of cognitive radar seeker. Firstly, we establish the processing flow of radar seeker waveform selection by combining the seeker guidance and observation filtering models; Secondly, we use the interactive multiple model unscented Kalman filter (IMMUKF) algorithm to solve the nonlinear tracking problem. We minimize tracking error by searching for waveform parameters based on the maximum mutual information criterion. The simulation results show that the waveform selection algorithm can effectively improve the seeker tracking accuracy and provide stable guidance overload. [ABSTRACT FROM AUTHOR]

Through flexible configuration of warship formation, the Aegis Warship Formation achieves interception and defense of targets in different attack directions. Based on the warship coordinate system and target coordinate system, the detection and interception zone models of Aegis warship formation are constructed. The functional simulation models of guidance radar and SM-3 interceptor are integrated to propose a dynamic simulation modeling method for anti-missile defense of warship formations. The detection directional coefficient and killing directional coefficient are defined, which are used to quantitatively evaluate the directional characteristics of the formation anti-missile defense. The simulations are conducted with a typical case of two-ship Aegis formation, and useful conclusions are obtained. The results can provide support for battlefield offensive and defensive situation assessment. [ABSTRACT FROM AUTHOR]

To meet the needs of flight teaching and training, aiming at the interactive, multi-input multi-output and comprehensive sequential logic controlling characteristic of integrated avionics system display and control interface for fighters, a functional simulation method by designing display and control logic independently is introduced. On the basis of this structure, a loosely coupled, modular and visual simulation method of display and control interface for multi-function fire control radar is presented, and the realization of display and control logic based on Simulink/Stateflow is introduced. The application result shows that the program progress is more intuitive, and the code is more maintainable, so this design method can improve the fidelity and developing efficiency of display and control simulation. All these researches can provide technology reference for the simulation of display and control interfaces for avionics systems and its subsystems. [ABSTRACT FROM AUTHOR]

In order to overcome the shortcomings of the traditional radar fault detection method, which is highly dependents on expert experience, consumes a lot of manpower and material resources, causes over-repair, cannot give advance warnings for degradation faults, and so on. An unsupervised radar degradation fault prediction method based on dynamic updated-neural network was proposed. Firstly, the historical data of peak power and operating frequency were collected by microwave measurement equipment. Secondly, the dynamic updated-neural network was used to dynamically update the historical data and predict the subsequent data. Finally, the isolated forest method was adopted for unsupervised fault detection on the predicted data. In this way, radar degradation fault prediction and early warning can be realized. The results show that the method proposed can predict degradation faults at least 10-time steps (100 minutes) in advance and give the real-time alarms. It also can realize radar degradation fault prediction when there are small samples, no fault samples, no feature extractions and no artificial thresholds. [ABSTRACT FROM AUTHOR]

All pineapples are harvested manually at present in China. But manual harvesting cannot fully meet the large-scale production against the ever-increasing greying workforce. Fortunately, automatic navigation can be expected to develop the pineapple harvester. In this study, a path planning algorithm was proposed as the navigation scheme to improve the mechanization and automation level of pineapple harvesting. An improved RRT* algorithm was also used for global path planning. Firstly, the self-heuristic idea was used to constrain the generation range of sampling points. Then, the bias probability pbias was introduced to generate the random sampling points. Specifically, the sampling points were randomly generated with the probability p in the space, when p>pbias. Otherwise, the target point was used as the sampling point, in order to decrease the blindness of sampling point generation. Thirdly, the gravitational field of the artificial potential field, and the concept of direction weight were introduced in the new node expansion. The weights wg and wk were assigned to the directions of the sampling and target point, respectively, where the direction was constrained in the expansion of the new node. Fourthly, the bidirectional expansion was used to speed up the iteration speed in the double-tree expansion. Finally, the greedy algorithm was applied to prune the redundant nodes of the path. The Cantmull-Rom interpolation function was also used to smooth the path corners. Three environments (including multiple obstacles, mazes, and narrow passages) were created to simulate the path planning process, in order to evaluate the performance among the improved navigation path planning, RRT* and bidirectional RRT* algorithm. Planning time, node number, and path length were selected as the indicators. Each algorithm experimented with 30 times in every single environment. The average, maximum, minimum, and standard deviation were calculated for the simulation data of the three indicators, respectively. The simulation results showed that the average planning time of the path planning algorithm in the three environments was 17.97%, and 46.12% higher than that of the RRT* and bidirectional RRT* algorithms, respectively, while the average programming speed was 4.7, and 2.2 times as rapid as that of the RRT*, and bidirectional RRT* algorithm, respectively. Furthermore, the average node number was 87.22% and 52.52% less than that of the RRT* and bidirectional RRT* algorithms, respectively. The average path length was 3.81% and 6.08% less than the RRT* and bidirectional RRT* algorithms, respectively. The field test showed that the planning time was only 14.12% and 20.34% of the RRT* and bidirectional RRT*, respectively. The iteration number was 80.89% and 69.70% less than that of the RRT* and bidirectional RRT*, respectively. In addition, the rotation angles larger than 60° on the path planned by RRT* and bidirectional RRT* algorithms were 1.56 and 2.06 times as much as that of the improved, respectively, and the rotation angles larger than 100° on the path were 1.55 and 2.18 times. The improved RRT* algorithm can fully meet the path navigation requirements of agricultural machinery in the field. The pineapple harvester can run along the planned path to the target point as moving with the speed of 0.2, 0.4, and 0.6 m/s, but the position and heading deviation increase with the moving speed. This finding can provide a sound reference for the navigation development in agricultural machines. [ABSTRACT FROM AUTHOR]

According to the remote sensing monitoring method of the ground deformation before mine landslide, the discrimination basis of the reliability of different orbit SAR data sets was proposed, and the inducing factors of landslide were analyzed. Taking the slope of the Anqian Yabaling open-pit mine in Anshan city as an example, using 44 Sentinel-1 radar images(two groups of ascending orbits and one group of descending orbit), the time series InSAR method was used to analyze the surface movement characteristics of the slope 6 months before the landslide occurred on November 25, 2019. The results show that the monitoring results obtained by different orbit data sets in the same studied area are different. The position of the top and rear edge of the landslide area showed an unusually strong acceleration within a period of time(approximately 45 days) before the landslide. The research results can provide new ideas for early identification of landslide hazard in mining areas using InSAR technology in the future. [ABSTRACT FROM AUTHOR]

Objectives: Two earthquakes with a distance less than 40 km occurred in Menyuan, Qinghai Province, China, including Mw 5.9 earthquake on 26th January 2016 and Mw 6.7 earthquake on 8th January 2022. Methods: The interferometric synthetic aperture radar (InSAR) technique was utilized to process Sentinel-1A radar images from both ascending and descending tracks to retrieve high resolution coseismic surface displacement maps for these two Menyuan earthquakes. The coseismic surface displacements were then inverted to determine the fault geometry parameters and the non-uniform fault slip distributions with a dislocation model in an elastic half- space. Results: The results show that the 2016 Menyuan earthquake was a reverse event on a buried fault with a maximum surface displacement of 6.7 cm and 7.0 cm on the ascending and descending tracks, respectively. The maximum slip on the fault plane reached 0.53 m and concentrated at depths of 4 − 12 km. The 2022 Menyuan earthquake ruptured along the NWW - SEE strike with a maximum surface displacement of 78 cm in the satellite radar line of sight and a maximum fault slip of 3.5 m occurred at a depth of approximately 4 km. Conclusions: Considering the tectonic settings around the Lenglongling fault zone, the 2022 Menyuan earthquake is likely to occure in the western segment of the left‐lateral Lenglongling fault, with its western end extending westward to the Tuolaishan fault. By calculating the coulomb failure stress changes (CFS), it is inferred that the 2016 Menyuan earthquake generated considerable positive CFS which might have triggered the 2022 Menyuan earthquake. [ABSTRACT FROM AUTHOR]

For flying-wing aircraft, the sharpening of the leading edge of the inner wing section can reduce the intensity of the specular reflection of the radar echo, thereby improving the stealth performance. Aiming at the aerodynamic problems caused by the sharpening of the leading edge, simulations are carried out on the flow field and electromagnetic scattering characteristics of a typical flying-wing aircraft. RANS equation is used as the governing equation, and the k-ω SST turbulence model is used to solve the problem. The aerodynamic performance of the flying-wing aircraft is analyzed, and the flow field characteristics are combined to explain. Using the multi-layer fast multipole algorithm, the simulation calculation of the electromagnetic scattering characteristics is carried out under the typical incident frequency. The results show that the leading edge sharpening modification of the inner wing section of the flying-wing layout aircraft can effectively reduce the forward radar cross section (RCS) and increase the penetration capability. But at the same time, it will bring about problems such as reduction of stall angle of attack under takeoff and landing conditions. By selecting an appropriate leading edge sharpening range, the situation can be improved, so as to have engineering application conditions. [ABSTRACT FROM AUTHOR]

To address the problems of the traditional regional road network path guidance system, such as the difficulty to effectively plan the optimal solution of the path and poor stability, the immune genetic algorithm was applied to the path guidance system. It connects the radar devices, infrared beacon identification and other devices with the path-induced control center to construct the hardware environment of the induction system. Then the immune genetic algorithm was used to expand the encoding of different initial points to determine the encoding path. Finally the optimal induction path was designed through the process of data evaluation, chromosome determination, path judgment and weight evaluation by combining the results of hierarchical search method and restricted region search method. The results show that the stability of the path guidance system using the proposed method is better, and the stability can reach 0.979. The intelligent path guidance system based on immune heredity can effectively determine the optimal solution of vehicle driving path, and can successfully achieve the optimal path guidance. [ABSTRACT FROM AUTHOR]

To meet the demand of large number and high integration of filters in the IF filter modules of radar receivers, a miniaturization LC filter based on the 3D integration technology is proposed in this study. The application and integrating process of 3D LC filter in modules are studied and analyzed. The 3D LC filter adopts two substrates, and uses 3D integration process and BGA technology to realize the three-dimensional integrated assembly of the two substrates. High-density layout can be realized by placing capacitive elements on the bottom substrate. Inductive elements are placed on the top substrate and separated from the capacitive elements, and signal communication is achieved through solder balls. The use of a three-dimensional integration scheme can reduce the size of the LC filter circuit by nearly 50%, and the filter index is consistent with the performance of the planar filter circuit. Compared with the traditional IF filter component design method, this solution is more conducive to achieve high integration and miniaturization. [ABSTRACT FROM AUTHOR]

An accurate and rapid extraction of fruit tree canopy has been one of the most important steps to determine the precise application of pesticides in modern agriculture. However, the current detection cannot fully meet the harsh requirements in the complex environment of an orchard in recent years. In this study, a novel extraction system was proposed to identify the fruit tree canopy in an orchard using millimeter-wave radar. Firstly, the region of interest (ROI) was set to remove a large number of environmental point clouds, and then the random sampling was used to fit and segment the ground point clouds. Secondly, a density-based clustering was selected to integrate the resolution of millimeter-wave radar and the distance parameter of the point cloud. An adaptive density clustering was then established to deal with the different densities of the point cloud at different distances when using the millimeter-wave radar. As such, the neighborhood radius was adaptively adjusted in different directions, according to the distance from the point cloud to the radar. The point cloud density was then used to identify the fruit tree canopy, further improving the recognition performance of the point cloud. Finally, an Alpha-shape was selected to reconstruct the three-dimensional surface of the fruit tree. The optimal parameters of the three-dimensional reconstruction were achieved to evaluate the volume from Alpha values and the geometric measurement. The random sampling was also used to extract the structural parameters. The parameters of the mathematical model were estimated from a data set with a large number of outliers using multiple iterations. A curve model was then obtained closer to the actual situation. In addition, a field test was conducted to verify the feasibility of the model in the Loquat Plant Resource Garden (113.367 789°E, 23.164 129°N) of South China Agricultural University on April 2th, 2021. 40 yellow-bark fruit trees were also scanned in the test. The results showed that a higher performance was achieved to effectively identify and extract the point clouds of a single tree canopy, where the F1 score was 93.7% for the recognition accuracy of fruit trees. Furthermore, the average relative errors of the plant height and crown width in the fruit trees were 8.7% and 8.1%, respectively, while the coefficients of determination were 0.84 and 0.92, respectively, and the root mean square errors were 16.39 and 7.82, respectively, compared with the manual measurement. In addition, the average volume of fruit trees was 5.6 m³ using Alpha-shape, increasing by 59.4% in the accuracy of volume, compared with the traditional. Nevertheless, two recommendations can be addressed during this time: 1) To identify the fruit trees with the overlapping canopies using a relatively higher resolution of millimeter-wave radar; 2) To quantitatively extract the volume of the fruit tree in the harsh sense. Anyway, the millimeter-wave radar can be widely expected to accurately extract the canopy information in an orchard. The finding can provide a new promising technology to extract the canopy information for the data collection and automatic operation in modern agriculture. [ABSTRACT FROM AUTHOR]

A beam dwell time allocation algorithm based on Riemannian manifolds is proposed for the spatial search process of digital array radar. In order to compensate for the gain loss resulting from beam broadening as it scans angles of the broadside，the spatial distribution of beam dwell time in uv-plane is given according to Riemannian manifolds. The dwell time is allocated optimally over the whole space and the transmitting power of beam is minimized under the condition that the one detection probability and the cumulative detection probability of the specified range are satisfied. The simulation results show that the beam dwell time allocation algorithm based on Riemannian manifolds eliminates the effect of beam gain variation caused by the change of direction，and realizes the equal power detection in different directions. [ABSTRACT FROM AUTHOR]

Objectives: The Sichuan-Tibet railway is not only a state key project in China, but also the most difficult super project in railway construction history due to the most complex terrain and the active geological structure. Interferometric synthetic aperture radar (InSAR) has proved to be a critical tool for detecting and monitoring geohazards because of its wide coverage, high precision, and high efficiency. However, due to its side-looking geometry, InSAR faces serious geometric distortions in mountainous areas, particularly in extremely steep high mountain and gorge areas along the Sichuan-Tibet railway. Methods: Utilizing the full open-access Sentinel⁃1 data, which has the wide coverage and a short revisiting time, we carry out satellite radar geometric distortion and feasibility analyses along the Sichuan-Tibet railway. Taking into account synthetic aperture radar(SAR) imaging geometry, the variations of the incidence angles across the whole SAR image, and the passive geometric distortions, we present an integrated approach to accurately determine the areas with SAR geometric distortions. Results: Our results for Sentinel-1 data show that the areas with geometric distortions for observations with a single-orbit (i.e. ascending or descending) reach 31%—35% along the Sichuan-Tibet railway, whilst the geometric distortion areas reduce to 1.5% for observations with both ascending and descending orbits; furthermore, about 35% of the whole area is suitable for joint analyses with both ascending and descending observations. Conclusions: The quantitative result also reveals the feasibility to use the Sentinel-1 data to detect and monitor geohazards along the Sichuan-Tibet railway from the perspective of geometric distortions. It can not only improve the overall understanding of the applicability of Sentinel-1 based InSAR and the InSAR results interpretation in this area, but also provide a guideline for efficiently selecting other satellite SAR datasets for detecting and monitoring geological disasters along the Sichuan-Tibet railway. [ABSTRACT FROM AUTHOR]

Radar echo extrapolation has been widely used in short-term heavy precipitation nowcasting. Aiming at the problem that traditional radar echo extrapolation methods do not make full use of massive historical meteorological data, which leads to low forecast accuracy, this paper proposed a new radar echo extrapolation method based on DPCNN. With the addition of dynamic probability calculation layer, DPCNN was able to calculate the corresponding probability convolution kernel for different radar echo input sequences and used it in the subsequent extrapolation calculations. Dynamic probability calculation layer enabled the network to make corresponding probability adjustments according to different input sequences in the prediction stage and enhance the connection between extrapolation and input sequences. The extrapolation experiment of short-term heavy precipitation in a certain area verifies the validity of the model from four aspects: extrapolated image, CSI, FAR, and POD. [ABSTRACT FROM AUTHOR]

Objectives: Zhouqu County in Gansu Province is highly prone to landslides. On July 12, 2018, a landslide blocked the Bailong River near the county, posing a serious threat to the life and property of local residents and the safety of infrastructure. The early identification of potential landslides around this area is of great significance to protect the life and property of local residents and the security of its infrastructure. Methods: Firstly, small baseline subset interferometry technology (SBAS‑InSAR) was adopted to identify the potential active landslides in the surrounding area of Zhouqu County, with the Sentinel‑1A satellite ascending and descending images from October 2017 to December 2018. Then, a combination of optical remote sensing image interpretation and field investigation is used to determine and identify potential landslides. The visual interpretation method can be used to identify landslides that have occurred or are occurring on the optical images. The field investigation was used to verify the landslide results identified by In‑ SAR and optical imagery to increase the effectiveness and accuracy of landslide detection. Results: A total of 23 active landslides were detected in the study area, with 16 active landslides detected on the ascending images and 11 on the descending images, with 4 active landslides having valid deformation information detected on both the ascending and descending images. Combined with the visual interpretation of optical remote sensing images and field investigation, the deformation characteristics of 4 typical landslides, including Jiangdingya, Mentouping, Suoertou, and Xieliupo Landslides, were analyzed. It was found that the deformation rates of Jiangdingya, Mentouping, and Xieliupo Landslides were significantly higher during the rainy season than during the non ‑ rainy season, and the Suoertou Landslide did not show a significant increase in the deformation rate during the rainy season but showed fluctuations in the deformation rate during the rainy season. Conclusions: The use of single‑orbit radar satellite data for landslide detection can easily lead to the missed detection of landslide hazards, and the combined use of ascending and descending radar images can improve the accuracy of landslide detection in steep mountainous terrain. The deformation rates of active landslides in the study area have a good correlation with rainfall. The results can provide supports for the geohazard mitigation and prevention in Zhouqu County. The strategy which combines optical imagery, field investigation, and time‑series InSAR can efficiently and accurately detect and monitor landslides over a wide area. [ABSTRACT FROM AUTHOR]

For the optimization of anti-jamming agile waveform for helicopter borne fire control radar, an agile phase coding waveform suitable for multi-pulse and short code length is designed based on singular value decomposition (SVD) and cyclic algorithm. First, we construct the constrained quadric optimization problem with low sidelobe ambiguity function in the target region. Second, the quartic is transformed into the quadric according to SVD decomposition, and an iterative convergence algorithm is designed. Third, a quadric iterative convergence algorithm is deduced based on the cyclic algorithm. Finally, the simulation results show that the two algorithms are close to each other in the design of agile waveform optimization performance, but the SVD decomposition algorithm has faster convergence speed, and the cycle calculation algorithm has higher operation speed. [ABSTRACT FROM AUTHOR]

Surface ruptures and deformation of large earthquakes are important for investigating earthquake mechanisms, fault activities and continental deformation. With the improvement of satellite techniques, op‐ tical and radar images have been widely used in earthquake studies since the 1992 Landers earthquake. However, due to a lack of pre ‐earthquake images, historical earthquakes prior to the 1990s are rarely studied. Recent declassification of American KeyHole (KH) satellite images opened up new possibilities of investi‐ gating old earthquakes back to the 1970s. Researchers have successfully applied KH ‐9 images to the 1978 Tabas‐e‐Golshan and 1979 Khuli‐Boniabad earthquakes in Iran, and gained some new insights into fault be‐ haviours. We first provided a review of the methodology and progresses of using KH ‐9 images to measure earthquake deformation, then investigated the 1976 Chaldiran, Turkey earthquake by matching the pre ‐ and post‐earthquake KH ‐9 images,and obtained an E ‐W displacement of about (3.1±0.7) m (i.e. strike‐slip), consistent with the measurements in the field. KH ‐9 imagery provides a new means of investigating histori‐ cal earthquakes in detail, but there are some limitations. These limitations are briefly discussed in the end. [ABSTRACT FROM AUTHOR]

Today, Sentinel ‐ 1A data with terrain observation with progressive scanning (TOPS) imaging mode is increasingly used in earth observation aiming at consistent monitoring of surface change and its deformation. However, due to the limited accuracy of coarse co ‐registration, spectral aliasing along the azimuth direction enables the presence of phase jumping in overlapping area of neighboring bursts. Although geometrical co‐registration in conjunction with enhanced spectral diversity (ESD) is proven to be a feasible strategy to correct such error and has been widely used in some open source softwares (e.g. DORIS (Delft object‐oriented radar interferometric software), SNAP (sentinel applications platform), ISCE (InSAR scientific computing environment)), the performance of ESD is still not satisfactory and relies strongly on spatiotemporal decorrelation. Given that decorrelation is generally quantized by interferometric coherence, this paper presents and assesses a new methodology to improve the accuracy of ESD by fully exploring the high coherent targets. Specifically, this method focuses on mitigating the spatiotemporal decorrelation in fine coregistration procedures by: (1) Selecting stable targets with moderate and high coherence using accurate coherence estimation. (2) Maximizing coherence magnitude by optimal interferometric subset chosen from Dijkstra algorithm. We compare and test this method against current favorites based on single master image and network‐based enhanced spectral diversity (NESD), and the experimental results demonstrate the value of our method. It can make up for the shortage of NESD method. [ABSTRACT FROM AUTHOR]

In the process of detecting and identifying W1dergroW1d coarse roots by groW1d - penetrating radar ( GPR), many factors will interfere and affect the resolution of root recognition. To solve this problem, the study firstly simulated tree roots through GprMax forward modeling, and simulated three main influencing factors in root detection, namely, different root diameters, different buried depths of roots and different radar scanning angles, then discussed the influence of three factors on root detection. Secondly, the forward simulation experiment and field experiment are compared to verify the effectiveness and feasibility of forward simulation. We found that the coarse root with a diameter of more than 5 mm and a shallow burial depth are easy to he detected when the scanning angle of radar is greater than 45 ° . These results confirmed that the diameter of root, the depth of root, and the scanning angle of radar were important influence factors for root detection, in addition, the results can improve the accuracy and precision of the NDT of tree root system. [ABSTRACT FROM AUTHOR]

For multiple-input multiple-output over-the-horizon radar( MIMO-OTHR),the minimum variance distortionless response( MVDR) is usually used to solve the impact of the ionospheric multimode clutter in the received echo on the detection of the target. The output signal-to-clutter-plus-noise ratio( SCNR) of traditional MVDR algorithm is low due to the omnidirectional emission of MIMO-OTHR,the inaccurate estimation of interference-noise covariance matrix,and a few training samples within a finite coherent accumulation time. Therefore,the MVDR algorithm is improved to enhance the multimode clutter suppression ability of MIMO-OTHR. A transmit beamspace preprocessing is realized by a second order cone programming( SOCP) to focus the energy on the principal mode angle,which is the transmit angle of the target path reflected by the ionosphere. The echo signal received by array is spatially smoothed to suppress the expected signal of principal mode and improve the estimation accuracy of interference-noise covariance matrix. A bi-iterative MVDR beamforming algorithm based on the beamspace preprocessing is proposed to reduce the computational load and the requirement of training samples. Simulated results show that the improved MVDR algorithm can be used to effectively suppress multi-mode clutter,and obtain higher output SCNR with fewer training samples and less computational load. [ABSTRACT FROM AUTHOR]

Aiming at the serious problem of de - coherence in the forest classification of the C - band synthetic aperture radar im¬age in conjunction with the optical image, a method of averaging coherence coefficient images in multiple periods in winter obtained by coherent imaging of similar time - phase radar images is proposed to effectively suppress the de - coherence phenomenon to obtain co¬herence coefficient images related to forest tree species. This paper uses the Sentinel - 1 backscattering intensity, coherence coefficient and JL101A image to classify the tree species of the moon lake national forest park in Changchun. The results show that when using on¬ly JL101A for classification, the overall classification accuracy is 82. 3% , Kappa coefficient is 0. 79； when using JL101A data and Sentinel - 1 intensity data for classification, the overall classification accuracy is 85.2% , and the Kappa coefficient is 0. 825 ； when u- sing JL101A data, Sentinel -1 intensity data and coherence data for classification, the overall classification accuracy is 87. 8% , and the Kappa coefficient is 0. 855. After using JL101A data, Sentinel - 1 intensity data and coherence data to classify, compared with u- sing only JL101A data, the precision of user accuracy is increased from 59% to 72%. It shows that the optical image combined with the effective coherence coefficient and backscattering intensity of C band synthetic aperture radar image can improve the classification accuracy of forest tree species. [ABSTRACT FROM AUTHOR]

Aiming at tlie problems of long period，low efficiency and poor versatility in radar rotating transmission combination system design， the method of modular design was put forward. The main advantages of tlie rotating transmission combination system based on modular design were analyzed. According to the radar technical requirements，the function modules of tlie rotating transmission combination system were de signed， and key technologies adopted in the process of tlie module design were expounded. The performance of rotating transmission combination system was tested. The results indicate that all the properties meet the rotating transmission requirements of radar for multi-media such as power， signal， optical， and liquid. [ABSTRACT FROM AUTHOR]

To solve the problem of high precision calibration of three‐dimensional baseline of space‐borne distributed interferometric synthetic aperture radar （InSAR） , a new method of baseline calibration is proposed. Firstly, this method combines the slave radar Doppler equation and the slave radar distance modification equation to form the baseline calibration model. Based on the ground control points, the least square iteration is used to solve the baseline three axial errors. Compared with the baseline calibration model based on the slave radar distance modification equation, the baseline calibration model optimizes the coefficient matrix condition number of the normal equation, reduces the morbidity of the matrix, and improves the calibration precision. Then, according to the baseline calibration, the baseline vector is actually used to intersect by the ground control points, and the intersection angle can be increased to improve the accuracy of the intersection. The control point layout strategy is given, that is, the control points are laid separately at the near and far ends of the radar footprint at ground. Compared with the way of laying control point uniformly, we can significantly improve the calibration precision and robustness. Finally, the spaceborne distributed InSAR simulation data was used to carry out the baseline calibration experiments based on different control points number, different control points precision, and different control points layout. The experiments show that the new method can achieve high ‐ precision result of three ‐ dimensional baseline calibration, namely, millimeter‐scale in cross‐track, along‐track and radial baseline direction respectively, which can provide a new way for the baseline calibration of spaceborne distributed InSAR . [ABSTRACT FROM AUTHOR]

In order to solve the problem of high time complexity in the signal recognition process by using convolution neural network, this paper proposed a new algorithm based on denoising auto-encoder and convolution neural network. Firstly, it performed the STFT on the radar emitter signal to obtain the time frequency image. And it used gray and threshold. It put processed image vectorization into the denoising auto-encoder. Then the denoising auto-encoder extracted the feature data. It put the reconstituted image matrix into the convolution neural network and used softmax classifier as classification and recognition. The simulation shows that the model using DAE is better than the original model and the time complexity decreases greatly. The recognition rate can reach 80% under SNR = - 6 dB. Besides, compared with the performance of traditional way, the recognition effect is obviously improved. [ABSTRACT FROM AUTHOR]

Adaptive waveform optimization technology is one of the key technologies of cognitive radar， which can improve the performance of radar systems. For the target detection， an adaptive waveform design method based on dual mutual information (DMI) criterion was proposed in the clutter environment. The mutual information (MI) was maximized by the proposed method between the received signal and the target impulse response while the mutual information could be minimized between the received signal and clutter impulse response. With the transmitted signal energy constraint， the optimization model for optimal waveform was established， and the optimal waveform was solved by the maximum marginal allocation algorithm (MMA). The simulation results demonstrate the validation of the proposed method. [ABSTRACT FROM AUTHOR]

Cohesive soil is a common soil medium in land resource evaluation and farmland land consolidation. The dielectric properties, physical, chemical properties and mechanical properties of cohesive soils are closely related to the soil water content. Ground penetrating radar (GPR), as a fast and non-destructive detection tool, can provide information about electromagnetic wave propagation in the ground, so as to realize the detection of underground media. Especially in soil water content detection, ground penetrating radar (GPR) has been widely used. However, due to the complex composition of cohesive soil and the fast attenuation of electromagnetic waves in cohesive, GPR is rarely used in the detection of water content in cohesive soil, which greatly restricts the application and development of GPR technology. The early signal of radar wave is the signal generated by the superposition of air wave and ground wave due to the small distance between the transmitting and receiving antenna, and it can also reflect the physical properties of the surrounding medium. The results show that there is a certain correlation between the early signal amplitude, waveform and soil water content. For cohesive soils, most of the radar wave energy has been lost near the surface due to the high conductivity, so ground waves are more useful waveform signals than reflected waves. In order to verify the accuracy of GPR, AEA (average amplitude envelope) method in detecting the water content of cohesive soil, this study used GPR to detect the soil water content of field farmland (<0.3 m) before and after rainfall, and TDR was used for comparison. The results showed that there was a certain correlation between soil water content and clay content. In a large area (1 000 m long survey line), the average soil water content detected by TDR was 14.16, 16.91 cm³ /cm3, respectively. The average soil water content detected by AEA method was 14.62, 17.88 cm³ /cm3, respectively, and the difference between them and the measured water content by TDR was 0.46, 0.97 cm3 /cm3, respectively. The water content detected by the two methods had extremely significant correlation (P<0.01), and the correlation coefficients were 0.825 and 0.814, respectively (P<0.01). Within a small range (40 m×40 m), the water content of cohesive soil detected by TDR before and after rainfall was 14.11, 16.77 cm³ /cm³, respectively. The average soil water content detected by AEA method was 14.86, 17.46 cm³ /cm³, respectively, which were 0.74, 0.69 cm3 /cm³ higher than that measured by TDR. The correlation coefficients of water content detected by the two methods were 0.701 and 0.827, respectively (P<0.01). Analysis of the water cut plane distribution of the two detection methods showed that the soil water content detected by AEA method was similar to TDR method. The results showed that ground penetrating radar (GPR) AEA model could accurately detect the water content of cohesive soil. The conventional GPR common mid point method and fixed offset method were used to detect the water content of cohesive soil in the study area. The results showed that neither of the two methods could effectively detect the moisture content of cohesive soil. [ABSTRACT FROM AUTHOR]

The traditional paramctcr-bascd signal sorting system cannot adapt to the problem of radar signal sorting in the current complex situation, and the blind source separation algorithm based on independent component analysis (ICA) is introduced into the radar signal sorting algorithm. FastICA algorithm combines fixed-point iteration and non-Gaussian maximization algorithm. It lias the advantages of good stability, fast convergence and small calculation. However, this algorithm is very sensitive to noise and cannot be performed with low SNR. Aiming at this shortcoming, a synchronous cumulative average noise reduction algorithm is introduced. and signal equalization and smoothing to improve the original algorithm are combined, so that the new algorithm can sort the radar signals with low SNR. Simulation results show that the improved algorithm can achieve good sorting effect under low SNR and retain the advantages of the original algorithm. [ABSTRACT FROM AUTHOR]

In the distributed resource management of shipborne phased array radar network,the inconsistent problem of resource management schemes is due to the estimated deviation of node states and the noise disturbance during information transmission between nodes. A consistency management algorithm for resource which is suitable for different topologies is proposed to solve the problem above. The topology of radar network is represented by using the graph theory,and the requirement analysis of consistency resource management is conducted for shipborne phased array radar network. A local optimization model for resource management is constructed from two aspects: radar load balancing and minimum resource consumption. A local resource management scheme of each radar is generated through this model. The consistency algorithm is used to obtain a global consistency resource management scheme through the information transmission between radar nodes of network. Simulated results show that the proposed algorithm is effective,and it can be applicated to conduct resource management for radar networks regardless of the topology and information transmission noise. [ABSTRACT FROM AUTHOR]

Three-dimensional phase unwrapping is one of the key steps of time-series interferometric syn- thetic aperture radar (InSAR) technology. The unwrapping results directly affect the accuracy of time-series InSAR land subsidence monitoring. Aiming at the problem of the whole period unwrapping error caused by phase undersampling in areas with serious land subsidence and large change of terrain slope, a weighted least squares phase unwrapping algorithm based on frequency confidence is proposed, which replaces the weighted least squares phase unwrapping algorithm based on phase gradient in the time-dependent three-di- mensional phase. By improving the accuracy of phase slope change estimation, the accuracy and stability of spatio -temporal three-dimensional phase unwrapping can be improved. The land subsidence results of the typical area in Beijing are obtained by using the improved spatio-temporal three-dimensional phase unwrap- ping method. Compared with the classical spatio - temporal three - dimensional phase unwrapping method, the improved method can obtain higher accuracy of land subsidence monitoring results. Especially for the subsidence funnel region where the slope changes greatly and the decorrelation exists obviously, the subsid- ence monitoring accuracy has improved significantly. [ABSTRACT FROM AUTHOR]

Landslide is the second only to earthquake as one of the most serious geological hazards， which occur most frequently and cause the most serious losses. It poses a great threat to people9 s lives and property safety. In order to monitor dangerous landslides dynamically, GPRI-II groundbased radar system is used in this experiment to study the key technology of landslide deformation time series analysis, and the main process of ground-based radar data time series processing is formulated. It is applied to the stability monitoring of a road slope in Shaoxing，Zhejiang Province. The experimental results show that there is an obvious deformation area in the slope，which is located in the accumulation area of the construction site，and the maximum deformation is 3. 5 mm/d. At present，ground-based radar has the ability to monitor deformation with sub-millimeter accuracy when the observation conditions are satisfied. Ground-based radar has become one of the important means of disaster early warning and control. [ABSTRACT FROM AUTHOR]

In order to promote the wider use of ground-based synthetic aperture radar (GB-SAR) in the field of surface micro-deformation monitoring, this paper summarizes the main types of GB-SAR and introduces the working principles and important parameters of some representative systems. By taking the S-SAR (slope-synthetic aperture radar) ground-based radar independently developed by the China Academy of Safety and Production Science as an example，the key processing techniques utilized by the GB-SAR are introduced. Experiments verify the accuracy of GB-SAR. It also shows the application of GB-SAR in open pit mine slope monitoring，mine dumping site and landslide emergency rescue. Finally，the problems and suggestions of GB-SAR in monitoring are proposed. [ABSTRACT FROM AUTHOR]

As one important part of post-earthquake loss assessment, building damage detection has important significance for understanding the disaster in time, implementing the emergency response, and post-disaster reconstructing. In recent years the development of the multi-platform remote sensing and the high-resolution remote sensing technology have provided opportunities to building damage detection with high accuracy. In this paper， firstly，the basic building damage characteristics and the concrete classification criteria are introduced. Then，the common damage detection methods at home and abroad based on the remote sensing technology are concluded，including the single-temporal image detection of the post-earthquake, the change detection between the pre-earthquake and the post-earthquake images，and the damage detection based on the multi-sources data. Limitations and drawbacks of these methods are proposed. Finally, combining with some cutting-edge technologies in remote sensing domain，the latest research progress of building damage detection based on the high-resolution remote sensing technology，synthetic aperture radar technology，light detection and ranging technology 9 and oblique aerial photography technology are summarized in detail. [ABSTRACT FROM AUTHOR]

The advantage of deep neural network to automatically learn the deep characteristics of data was studied. This paper proposed a signal sorting method based on multilayer deep belief networks, in order to solve the problems of time consuming in traditional radar signal selection, feature redundancy and so on. Based on the improved algorithm of depth of stacked multilayer model, it overcame the problem of insufficient to the single model of learning ability, and deeply studied the essential features of the different signal, and fused the posterior probability of the model to make a classification decision, so as to further improve the signal recognition rate. It used this method to sort 7 different types of radar emitter signal sorting. Compared with other performance signal sorting method, the experimental results show that this method obtains better classification results, and exhibits strong learning ability to nature features, thus it verifies the effectiveness and superiority of this algorithm. [ABSTRACT FROM AUTHOR]

A specific emitter identification( SEI) method based on deep reinforcement learning is proposed on account of the deficiency of emitter individual feature extraction depending on artificial experience. Due to the differences of the transient information of signal envelope,which results from the change of the signal owing to a nonideal transmitter channel,an envelope rising edge is used as the input state of deep neural network,and the emitter classifications are used as the optional actions of the current input state. The envelope features are extracted automatically through the convolutional neural network( CNN),and Q values of the current state action pairs are fitted,thus completing the specific emitter identification task based on the reinforcement learning model. The applications of deep Q network( DQN),deep double Q network( DDQN) and Dueling network in the specific emitter identification are discussed. The measured results show that the recognition rate of traditional machine learning algorithm is less than 80%,but the deep reinforcement learning model can achieve the high recognition rate of98. 42%. [ABSTRACT FROM AUTHOR]

Satellite remote sensing has made great strides in the last few decades, which enables the long-term consistent observations of many variables of the terrestrial water cycle and thus advances the understanding of the terrestrial water cycle. This paper reviews the principles of remote sensing in retrieving key variables of the terrestrial water cycle, illustrates the progress of satellite remote sen-sing in hydrological applications, and discusses the future direction. Although most of hydrological states and fluxes variables are observable by remote sensing, closing terrestrial water budget with the remote sensing products is still an open question, suggesting more efforts are needed to improve the hydrological consistency of the remote sensing products. In the future, efforts should be made to develop new generation sensors and platforms for the consistent remote sensing products with finer spatiotemporal resolution. At the same time, efforts should be devoted to the evaluation of remote sen-sing products of the terrestrial water cycle through carrying out integrated field experiments. [ABSTRACT FROM AUTHOR]

To meet the requirements of accuracy and reliability of plant protection robot in ridge identification and route planning, also improve the working conditions of farmers, and achieve an unmanned operation purpose, an algorithm based on multi-support-vector proportioning weight of SVM (support vector machine) to identify the ridge line of vineyards, and the path planning of plant protection robots were proposed. The strategy first uses Kalman filter to pre-process coarse orchard data information obtained by Lidar scanning. According to the principle that Kalman filter complies with Gaussian distribution, the prior point between 2 adjacent points was taken as the prior state, and the latter point was used as the observed point to obtain posteriori state estimation, so as to realize data integration and analysis. With its good system state estimation characteristics, the collected data can be used to judge the trend of the ridge line, so as to correct the noise outliers in the data and improve the readability of the data. Then according to the characteristics of the vineyard branch ridge and the characteristics of ridges line with clearly separable spacing, and corresponding to the situation that the ridge line on both sides can be completely separated, the method was combined with SVM linear classification. With the unique advantages of the classification and due to that SVM can search the unique segmentation hyperplane, the maximum interval and segmentation hyperplane, classification margins in the ridge environment could be gotten. The split hyperplane obtained at this time would be between ridge lines. However, there was a big deviation from the angle of the direction of the ridge line and the horizontal distance. It could not meet the precise operation requirements of plant protection robots. It needed further data processing and analysis. In order to obtain accurate position of the center of ridge line, finally, the relative weights were assigned to the sample points of each ridge based on the geometric interval relationship between the sample points on both sides of the ridge and the corresponding SVM classification marginal line. The classification marginal line was reformed according to the number of sample points and the relative weights. According to the condition of the product of the interval relationship between each sample point and the classification margin, their relative weight must be consistent with the quality value of classification margin. The random sampling consistency iteration method (RANSAC) would avoid the error of cost estimate, and could estimate the parameters of the mathematical model from a group of observed data with outliers, so as to obtain the predicted safety location of the ridge. Although the pre-estimated security location of ridge line was not necessarily consistent with the actual location of the vineyard ridge, navigation line could be obtained indirectly by the security ridge line on both sides and the principle of angle bisector which could meet the requirements of precision operation of plant protection robot. Operation guidance line for plant protection robot could be acquired. After a number of actual samples were tested, the average angular deviation between the fitted navigation line and the actual ridge centerline was 0.72°, and the average distance deviation of the relative plant protection robot was 4.22 mm. Experimental results showed that this algorithm could effectively identify and locate the navigation route needed by the plant protection robot. The fitted navigation line could meet the requirements of accurate operation of the plant protection robot in the vineyard. However, the redundancy of the algorithm was relatively large, and the time required to process data in a single time was about 2.05 s. With the accelerated calculation speed of the processor in the future, the algorithm provided in the article can provide a reference solution for such a problem. [ABSTRACT FROM AUTHOR]

When the interference enters from the direction of the main-lobe of the radar, adopting the adaptive beamforming algorithm will lead to problems such as beam distortion and migration of the main-lobe. Aiming at the problem of interference entering from the radar main-lobe, this paper proposed an anti-jamming method based on the design of the transmitted waveform. When the signal encoded by Frank is transmitted, it constructed the matched filter and the blocking filter by using the orthogonality of the Frank code, and passed the received data through the two filters to obtain two signals. Then it suppressed the interference by the adaptive cancellation method, thus suppressed the main-lobe interference. Finally, simulation results show that the method of waveform design can effectively suppress interference from main-lobe entry. [ABSTRACT FROM AUTHOR]