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The data fusion of radar and automatic dependent surveillance-broadcast(ADS-B) is the effective method to surveille the 'black flights' and flying birds. However, the tracking performance of the two sensors has large differences and is easy to be fluctuated, which will bring a decline in fusion accuracy. A data fusion method of radar and ADS-B based on track quality assessment is proposed. Firstly, the effects of local track accuracy, data update times and sensor measurement errors on local track quality on corresponding assessment factors are analyzed and quantified. And then, these assessment factors are combined to calculate the quality weighting factors of the local track, and the asynchronous track fusion processing of radar and ADS-B is completed based on the distributed fusion structure. Finally, the feasibility and effectiveness of the proposed method is verified with simulation experiment and application. The results show that the proposed fusion method can effectively improve the fusion accuracy, and the tracking errors are better than the traditional algorithms when the sensor tracking performance fluctuates.

In the ground attack mission, the attack plan needs to be formulated and distributed to the attack aircraft based on the threat source intelligence data obtained in advance with the help of the tactical mission planning system. The passive radar reconnaissance and direction finding system can calculate the location information of threat sources and provide it to the task planning system by processing the received signal. When the enemy radar is a frequency diverse array system rather than a phased array system, the bending characteristics of the beam of the frequency diverse array radar can deceive the passive direction finding system. On the basis of analyzing the basic structure of frequency diverse array radar, this article compares the phase plane characteristics of phased array and frequency diverse array, and takes the interferometer direction finding system as an example to study the deception effect of frequency diverse array radar compared to phase method direction finding. The deception simulation results of several nonlinear FDA arrays show that the virtual transmitter intersection coordinate deviation of log-FDA is greater than that of sin-FDA. When the x-axis coordinate of the interferometer is xG=300 km, the virtual transmitter intersection coordinates gradually approach the actual position of the FDA array as the y-axis coordinate of the interferometer increases. The intersection offset is log-FDA>sin -FDA>reciprocal-FDA.

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]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most traditional multi-aircraft flight path optimization methods are oriented toward area coverage, use static optimization models, and face the challenge of model mismatch under complex dynamic environments. Therefore, this study proposes a flight path optimization method for dynamic area coverage based on multi-aircraft radars. First, we introduce an attenuation factor to this method to characterize the actual coverage effect of airborne radar on a dynamic environment, and we take the area coverage rate under the dynamic area coverage background as the optimization function. After integrating the constraints of multi-dimensional flight path control parameters to be optimized, we built a mathematical model for dynamic area coverage flight path optimization based on multi-aircraft radars. Then, the stochastic optimization method is used to solve the flight path optimization problem of dynamic area coverage. Finally, the simulation results show that the proposed flight path optimization method can significantly improve the dynamic coverage performance in dynamic areas compared with the search mode using preset flight paths based on multi-aircraft radars. Compared with the traditional flight path optimization method oriented to static environments, the dynamic coverage performance of our proposed method is improved by approximately 6% on average.

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]

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]

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]

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]

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]

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]

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]

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]

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]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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 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]

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]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A large number of sensors are deployed in the new generation phased array radar for T/R components, which provides prerequisites for their data-driven fault diagnosis. However, actual detection data lack labels representing specific fault mode. Combining the advantages of deep belief network (DBN) in feature self-learning and the ability of auto-encoder (AE) to reconstruct input data, a fault feature extraction and intelligent diagnosis method based on DBN-AE semi-supervised learning model is proposed, and its structure is optimized by the firework algorithm (FWA). The original unlabeled data is directly used to train the DBN-AE model to extract deep feature, then the relation model between deep features and fault modes is built through supervised retraining. The proposed method is verified to enhance the fault diagnosis accuracy and intelligence of the T/R module on a certain phased array radar. [ABSTRACT FROM AUTHOR]

In radar detection of multiple targets, the large difference in echo energy of coexisting targets leads to the phenomenon that strong targets cover weak targets. To tackle this problem, a peak self-seeking track before detect (TBD) algorithm based on parallel line coordinate transformation is proposed. By comparing the sum of the maximum energy of measurement point in the parameter unit where each measurement point is located after accumulation, the unit with the maximum energy accumulation value is found, by which a new binary accumulation result is formed. Then peak value extraction, track back-tracking, and track correction are performed with the threshold. The simulation results show that the improved algorithm has a high global detection probability for multiple targets, and can initiate multiple targets with different echo energies. [ABSTRACT FROM AUTHOR]

The radar echoes of birds and drones target have high similarity, which make it difficult to distinguish them. Therefore, the spatio-temporal characteristics of target tracks formed by drones, birds and dynamic precipitation clutter are studied, and the differences in motion mechanisms and behavior patterns between drones and birds are analyzed. A motion feature extraction method based on target tracks is proposed and target feature vectors are constructed. Based on the measured track data of the target provided by the detection bird radar system, a training and test sample set is established. The supervised learning method combined with the random forest model is used to distinguish the target tracks of drones, birds and precipitation clutter. The experimental results show that the correct recognition rate of drone targets over a wide area can reach over 85%, and the classifier model has high calculation efficiency, strong sample adaptability, and good universality and practical value. [ABSTRACT FROM AUTHOR]

The inherent Doppler blind zone (DBZ) of airborne early warning radar can bring out target track termination with the worse effect in multi-target and clutter environment. Based on two-dimension assignment algorithm with DBZ information, a new plot-to-track association method based on multi-dimension assignment algorithm is proposed. Firstly, the evolution information of target track is used enough, and the radar's continuous multi-frame measurement data are used to perform sliding window processing. Secondly, the cost function of multi-dimension assignment is constructed. Finally, the solution of function on the condition of minimal cost is used as the targets plot-to-track assignment solution. The simulation results show that this method has further descent of track termination rate compared with two-dimension assignment algorithm in multi-target and clutter environment with DBZ. [ABSTRACT FROM AUTHOR]

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Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)