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When the satellite signal is lost or interfered with, the traditional GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System) integrated navigation will degenerate into INS, which results in the decrease in navigation accuracy. To solve these problems, this paper mainly established the PSO (particle swarm optimization) -LSTM (Long Short-Term Memory) neural network model to predict the increment of GNSS position under the condition of satellite rejection and accumulation to obtain the pseudo-GNSS signal. The signal is used to compensate for the observed value in the integrated system. The model takes the advantages of LSTM, which is good at processing time series, and uses PSO to obtain the optimal value of important hyperparameters efficiently. Meanwhile, the improved threshold function is used to denoise the IMU (inertial measurement unit) data, which improves the SNR (signal-to-noise ratio) of IMU outputs effectively. Finally, the performance of the algorithm is proved by actual road test. Compared with INS, the method can reduce the maximum errors of latitude and longitude by at least 98.78% and 99.10% while the satellite is lost for 60 s, effectively improving the accuracy of the GNSS/INS system in satellite rejection.

In this paper, we investigate the H

Uranium enrichment is considerably prevalent in Jurassic coal-bearing strata in the Yili Basin. A large amount of uranium deposits (occurrences) have been discovered in recent decades. Previous studies have found that uranium deposits and coal seam have a certain correlation in their genesis and spatial distribution or sometimes uranium deposits develop directly in the coal seam. What are the geological characteristics of uranium enrichment? How is uranium enriched? How to strengthen the cooperative development of uranium and coal and environmental protection? In order to explain the aforementioned questions, the characteristics of uranium deposits, rock minerals, and geochemical and metallogenic chronology are summarized herein, and the geological control mechanism of uranium enrichment in coal-bearing strata is discussed. It is found that uranium enrichment (including sandstone uranium deposits and coal uranium deposits) has multistage genetic characteristics and is mainly spread over the gentle slope of the southern margin of the Yili basin, with its host rock possibly being sandstone, coal, and sometimes even mudstone. The uranium concentration has a considerable correlation with the reductant, and the occurrence state of uranium has both inorganic and organic affinities. In addition, uranium enrichment is believed to be a comprehensive effect of high uranium source rocks, tectonic activity, sedimentary facies, hydrogeology conditions, paleoclimate, and reductant. The difference is that uranium enrichment in sandstone is often generated in a mud-sand-mud stratigraphic structure, while uranium enrichment in coal usually develops as coal-sand-mud. What is more, strengthening the study of physical and chemical properties of the host rock, strengthening the study of uranium occurrence state, and sharing geological data are important ways for the cooperative development of coal and uranium resources and environmental protection.

Cloud detection is one of the essential procedures in optical remote sensing image processing because clouds are widely distributed in remote sensing images and cause a lot of challenges, such as climate research and object detection. In this article, a lightweight deep-learning-based framework is proposed to detect cloud in remote sensing imagery. First, a multiple features fusion strategy is designed to extract learnable manual features and convolution features from visible and near-infrared bands. Then, a lightweight fully convolutional neural network (ClouDet) with a microarchitecture named dilated separable convolutional module is used to extract the multiscale contextual information and gradually recovers segmentation results with the same size as input image, which is more effective for large-scale cloud detection with larger receptive field, less parameters, and lower compute complexity. Third, context pooling is designed to amend the possible misjudgments. Visual and quantitative comparison experiments are conducted on several public cloud detection datasets, which indicates that our proposed method can accurately detect clouds under different conditions, which is more effective and accurate than the compared state-of-the-art methods.

In high spatial resolution remote sensing imagery (HRSI), ship detection plays a fundamental role in a wide variety of applications. Despite the remarkable progress made by many methods, ship detection remains challenging due to the dense distribution, the complex background, and the huge differences in scale and orientation of ships. To address the above problems, a novel, fully deformable convolutional network (FD-Net) is proposed for dense and multiple-scale ship detection in HRSI, which could effectively extract features at variable scales, orientations and aspect ratios by integrating deformable convolution into the entire network structure. In order to boost more accurate spatial and semantic information flow in the network, an enhanced feature pyramid network (EFPN) is designed based on deformable convolution constructing bottom-up feature maps. Additionally, in considering of the feature level imbalance in feature fusion, an adaptive balanced feature integrated (ABFI) module is connected after EFPN to model the scale-sensitive dependence among feature maps and highlight the valuable features. To further enhance the generalization ability of FD-Net, extra data augmentation and training methods are jointly designed for model training. Extensive experiments are conducted on two public remote sensing datasets, DIOR and DOTA, which then strongly prove the effectiveness of our method in remote sensing field.

Measuring optical transmission matrix has enabled image detection through scattering media, however its retrieved resolution is severely limited by the number of measurements. In this paper, we introduce super resolution reconstruction into transmission matrix based imaging scheme, to some extent bypassed this limitation. We demonstrate all detailed information of high-resolution subpixel shifting target are preserved in its corresponding speckle signals. And through phase conjugate reconstruction one can get target's low-resolution projection images with accuracy. The final high-resolution image is achieved by subpixel registration and bilinear interpolation operations. The feasibility of the proposed method is theoretically analyzed and proved by laboratory experiments.

This study combines data-driven-based logistic functions with prediction–area (P–A) plot for delineating target areas of orogenic Au deposits in the eastern margin of the Qinling metallogenic belt, central China. First, appropriate geological and geochemical factors were identified, optimized, and transformed into a series of fuzzy numbers with a range of 0–1 through a data-driven-based logistic function in order to determine the evidence layer for prospecting orogenic Au. In addition, the P–A plot was derived on the above evidence layers and their corresponding fuzzy overlay layers to pick out a proper prediction scheme, in the process of which acidic magmatic activity proved to be the most important factor of ore-controlling. Moreover, to further prove the advantages of this method, a traditional linear knowledge-driven approach was carried out for comparative purposes. Finally, based on concentration–area (C–A) fractal theory, the fractal thresholds were determined and a mineral prospecting map was generated. The obtained prediction map consisted of high, medium, low, and weak metallogenic potential areas, accounting for 2.5%, 16.1%, 38.4%, and 43% of the study area, containing 2, 3, 1, and 0 of the 6 known mine occurrences contained, respectively. The P–A plot indicated that the result predicted 83% of Au deposits with 17% of the area, confirming the joint application of the data-driven-based logistic function and P–A plot to be a simple, effective, and low-cost method for mineral prospectivity mapping, that can be a guidance for further work in the study area.

he present work combines data-driven based logistic function with prediction-area plot for delineating target areas of orogenic gold deposits in eastern margin of Qinling metallogenic belt, central China. Firstly, the values of geological and geochemical information layer were transformed into a series of fuzzy numbers with a range of 0-1 through a data-driven based logistic function on the basis of mineralization theory of the orogenic gold deposits. Secondly, the prediction-area(P-A) plot was performed on the above evidence layers and their corresponding fuzzy overlay layers to pick out a proper prediction scheme for mineral prospectivity mapping(MPM) based on the known gold occurrences. What’s more, to further prove the advantages of this method, we also used a knowledge-driven approach for comparison purpose. Finally, with the concentration-area(C-A) fractal model, the fractal thresholds were determined and a mineral prospecting map was generated. The result, five of the six known gold deposits are located in high and moderate potential areas (accounts for 18.6 % of the study area), one in low potential area (accounts for 38.4 % of the study area) and none in weak potential area (accounts for 43 % of the study area), confirmed the joint application of data-driven based logistic function and prediction-area plot a simple, effective and low-cost method for mineral prospectivity mapping, which can be a guidance for further work in the research area.

Feature point matching has been widely applied in image registration, image fusion, remote sensing and other fields. The relation between the pixels of infrared images and pixels of visible images is complex due to the images were taken by different sensors. Different sensors images also contain some common information which can been depended to achieve point matching. Scale Invariant Feature Transform (SIFT) algorithm is an effective and popular feature extraction algorithm. SIFT algorithm can be used in point matching, it can get feature descriptor vectors of the feature points which extracted from the images. But in some scenes, SIFT algorithm can’t achieve the accurate feature point matching. Different from SIFT algorithm, Edge-Oriented-Histogram (EOH) algorithm characters the orientation information of the edge and EOH feature descriptor can integrate the boundary information in different directions around the feature points, so that we can realize the describe of the edge direction and amplitude by EOH algorithm. To achieve the accurate feature point matching of infrared image and visible image, we propose a feature point matching method based on SIFT algorithm and EOH algorithm. Firstly, we use image enhancement to increase contrast of the image and then we use SIFT algorithm to extract feature points. Next, we utilize the EOH algorithm to get the 80 bins descriptors of the feature points which detected by SIFT algorithm. We calculate Euclidean distance to get the similarity of the descriptors to achieve point matching. In order to improve the matching accuracy, we adopt Random Sample Consensus (RANSAC) algorithm to eliminate the mismatching points. Nevertheless, RANSAC can’t get all correct points, we use another measure of Euclidean to select correct pairs, then we combine the two parts to match the feature points again. Experimental results demonstrate that our method can effectively improve the accuracy of matching and find more correct matching point pairs.

Unpolarized sunlight becomes polarized by atmospheric scattering and produces a skylight polarization pattern in the sky, which is detected for navigation by several species of insects. Inspired by these insects, a growing number of research studies have been conducted on how to effectively determine a heading angle from polarization patterns of skylight. However, few research studies have considered that the pixels of a pixelated polarization camera can be easily disturbed by noise and numerical values among adjacent pixels are discontinuous caused by crosstalk. So, this paper proposes a skylight compass method based upon the moment of inertia (MOI). Inspired by rigid body dynamics, the MOI of a rigid body with uniform mass distribution reaches the extreme values when the rigid body rotates on its symmetry axes. So, a whole polarization image is regarded as a rigid body. Then, orientation determination is transformed into solving the extreme value of MOI. This method makes full use of the polarization information of a whole polarization image and accordingly reduces the influence of the numerical discontinuity among adjacent pixels and measurement noise. In addition, this has been verified by numerical simulation and experiment. And the compass error of the MOI method is less than 0.44° for an actual polarization image.

Our previous work has constructed a polarized light orientation determination (PLOD) artificial neural network. Although a PLOD network can determine the solar azimuth angle, it cannot determine the solar elevation angle. Therefore, this paper proposes an artificial neural network for polarized light solar position determination (PLSPD), which has two branches: the solar azimuth angle determination branch and the solar elevation angle determination branch. Since the solar elevation angle has no cyclic characteristics, and the angle range of the solar elevation angle is different from that of the solar azimuth angle, the solar elevation angle exponential function encoding is redesigned. In addition, compared with the PLOD, the PLSPD deletes a local full connection layer to simplify the network structure. The experimental results show that the PLSPD can determine not only the solar azimuth angle but also the solar elevation angle, and the solar azimuth angle determination accuracy of the PLSPD is higher than that of the PLOD.

Unaided weak Global Positioning System (GPS) signals acquisition requires both long coherent integration time and a great amount of noncoherent integration operations. The acquisition is declared if the integration result crosses a predetermined threshold. In this paper, four different noncoherent integration alternatives using different approaches to compute the noncoherent integration at each noncoherent integration step are introduced. The performance in terms of detection and false alarm probabilities is investigated. For weak signal acquisition, it is shown that Alternative 2 is nearly 3dB more sensitive than Alternative 1, and Alternatives 3 and 4 are better than Alternative 1 but worse than Alternative 2. Examples are given using Alternatives 1 and 2. They illustrate how to decide the thresholds to achieve a probability of detection for a given probability of false alarm, and serve as a guide to determine the combination of the coherent integration time and the number of noncoherent integrations. The comparison between Alternatives 1 and 2 is also processed, and the statistics of simulations have verified the conclusion that Alternative 2 is nearly 3dB more sensitive than Alternative 1.

One-stage or two-stage deep learning based methods have problems when performing infrared multi-target detection, namely low accuracy and low running speed. Inspired by regression ideas of two-stage anchor-based model from coarse to fine, we propose a high performance pseudo-two-stage model that is specific to infrared images, in order to make a trade-off. The model retains the fast speed of the one-stage detection model through the introduction of cascade regression. We designed a dual-pass fusion module (DFM) and adaptive channel enhancement module (ACEM) to implement infrared image key feature fusion and calibration. To further optimize the model, we exploited the cascade regression and hard example mining by analyzing the shortcomings of the current one-stage detection approach. We conducted comparative experiments on the FLIR ADAS dataset and our method obtained 75.57% mAP, which is about 5% and 13% higher than the Faster R-CNN two-stage model and the SSD one-stage model, respectively. We ran the model at 21.4 FPS on Geforce RTX 2080 Ti, which made it 3FPS slower than SSD. The promising results show that the proposed approach is effective and practical.

The rapid development of unmanned aerial vehicles (UAVs) has made great progress for its widespread uses in military and civilian applications in recent years. On-board integrated navigation sensors are essential for UAV flight control systems in that they must operate with robustness and reliability. To achieve this, timely and effectively anomaly detection capabilities for the estimated UAV status from the integrated navigation sensors are required to ensure the UAV flight safety. Extraction of the anomaly information from the real-time navigation sensors and designing a robust and reliable anomaly detection algorithm are major issues for the UAV navigation sensor anomaly detection. This study introduces a novel integrated algorithm for detecting UAV on-board navigation sensor anomaly, by combining particle filter (PF) estimated state residuals with fuzzy inference system (FIS) decision system. The residual information is obtained based on the difference between the collected Global Positioning System measurements and high accuracy PF estimates. The indicators derived from the PF residuals are further made as inputs for the FIS system to output the different anomaly levels. The simulation and filed test results have demonstrated the effectiveness and efficiency of the proposed anomaly detection method in terms of timeliness, recall and precision.

In (order to improve the navigation accuracy of launch vehicle in high dynamic, large airspace and high-speed flight conditions, a low-cost integrated navigation system based on GNSS/SINS in launch inertial coordinate system is proposed. In these flight conditions, influence of time synchronization on navigation accuracy is not negligible and it's difficult to obtain high-precision time synchronization information. Therefore, a novel algorithm to improve the time synchronization accuracy by using interpolation method is designed. Digital simulation and vehicle experiment illustrate that the position error is less than 5m, and velocity error is less than 0.2m/s. Thus, this GNSS/SINS integrated navigation algorithm can effectively improve navigation accuracy of launch vehicle.

When the three-loop autopilot parameters designed by linear systems are used on the actual nonlinear system, the actual flight control performance is often inconsistent with the design specifications. Therefore it is necessary to construct a simulation environment close to the actual flight conditions to perform further performance tests on the designed autopilot. The propose of this paper is to give a simulation method of autopilot performance test based on aerodynamic environment which uses the ballistic feature points to construct the simulation environment and determine the initial flight conditions. The matlab software is used to solve the ballistic equation. Combining with the non-linear constraints such as angle of attack and rudder deflection angle, the attitude stabilization process and overload tracking performance of missile under the action of autopilot are analyzed to evaluate the control performance of autopilot. Finally, by comparing the simulation results between aerodynamic and static environments, it is shown that this method can not only get the overload command adjustment time closer to the actual flight, but also get the system stability margin which varies with the angle of attack, so as to analyze the stability changes in the process of attitude adjustment. The difference between the practical application effect and design index of autopilot from two aspects of parameter design and non-linear constraints at specific angle of attack is explained by this paper, thus providing a new and effective method for theoretical design and control performance test of autopilot.

We combined cluster analysis, the fuzzy analytic hierarchy process (fuzzy AHP), and the concentration–area (C-A) fractal for mineral prospectivity mapping. First, cluster analysis was used to determine the indicator elements for orogenic Au deposits (omitting redundant geochemical elements). Subsequently, according to pairwise comparisons of mineralization alternatives performed by three exploration experts, a series of fuzzy evaluation matrices were constructed, and the corresponding normalized weights were calculated. Furthermore, on the basis of the multifractal theory, two thresholds for each alternative were obtained (each alternative was divided into a background region, a general anomaly region, and a high anomaly region), and the fuzzy membership function was used to obtain the normalized score of each alternative. Finally, the weight of each alternative was multiplied by the normalized score, and fuzzy superposition was employed to generate a mineralization prediction map, which consisted of high, medium, low, and weak metallogenic potential areas, accounting for 7.05%, 19.67%, 40.99%, and 32.29% of the study area, which contained 4, 1, 1, and 0 of the six known mine occurrences, respectively. The prediction area (P-A) plot indicated that the result predicted 83.3% of Au deposits with an area of 16.7%, indicating that the combination of cluster analysis, the fuzzy AHP, and the concentration–area method is an efficient and economical forecasting method that has guiding significance for mineral prospectivity mapping (MPM).

This paper proposes a novel wavefront-shaping-based focusing method, by introducing the differential evolution algorithm (DEA), thereby realising a faster convergence rate and improved enhancement compared to rival algorithms. Via simulations, we show that our proposed DEA-based approach delivers the best focusing performance irrespective of the influence of noise. Experimental results demonstrate that the DEA boosts the enhancement for an equivalent number of measurements compared with conventional optimisation methods. Furthermore, we reveal the influence of certain DEA parameters, leading to the emergence of many modified DEAs that perform impressively. The proposed DEA-based method simplifies the computational complexity and implementation process of wavefront shaping, offering useful insights for the future study of optimisation algorithms for wavefront shaping, as well as potential for practical applications, such as deep tissue focusing.

The austenite grain growth behaviour of new pipeline steel for deep sea was researched by using optical microscope. The results show that the microstructures are homogeneous and the grain size increases slowly while the soaking temperature increases from 1120°C to 1180°C. However, the abnormal growth phenomenon can be observed in the specimens reheated at 1210°C. The austenite grain growth behaviour can be described by following equations: lnD = 7.7738- 6242/T (1120°C< T< 1180°C) and lnD = 51.2948-69510/T (1180°C

Insects such as desert ants and drosophilae can sense polarized skylight for navigation. Inspired by insects, many researchers have begun to study how to use skylight polarization patterns for attitude determination. The Rayleigh sky model has become the most widely used skylight polarization model for bioinspired polarized skylight navigation due to its simplicity and practicality. However, this is an ideal model considering only single Rayleigh scatter events, and the limitation of this model in bio-inspired attitude determination has not been paid much attention and lacks strict inference proof. To address this problem, the rotational and plane symmetry of the Rayleigh sky model are analyzed in detail, and it is theoretically proved that this model contains only single solar vector information, which contains only two independent scalar pieces of attitude information, so it is impossible to determine three Euler angles simultaneously in real-time. To further verify this conclusion, based on a designed hypothetical polarization camera, we discuss what conditions different three-dimensional attitudes must satisfy so that the polarization images taken at different 3D attitudes are the same; this indicates that multiple solutions will appear when only using the Rayleigh sky model to determine 3D attitude. In conclusion, due to its single solar vector information and the existence of multiple solutions, it is fully proved that 3D attitude cannot be determined in real time based only upon the Rayleigh sky model. Code is available at: https://github.com/HuajuLiang/HypotheticalPolarizationCamera.

In recent years, there has been an increasing interest in object detection in remote sensing imagery with onboard sensors and embedded platform based on deep convolutional neural networks. However, the limited cost, power consumption, compute complexity, and parameter size make the task challenging. The current object detection frameworks are mainly designed on the basis of graphics processing units (GPUs) and require further optimization in power consumption, and calculating quantity and parameter size. To address these issues, we propose an effective single-shot multibox detector (DF-SSD) framework, using the DepthFire module we designed to reform SqueezeNet as the backbone network to reduce the calculating quantity and improve the processing efficiency. To evaluate the effectiveness and superiority of DF-SSD, compared with the other state-of-the-art methods, extensive experiments are conducted on various hardware platforms, including GPU 1080ti, central processing unit i7-7700k, NVidia Jetson TX2, and Cambricon-1H8. Experiment results show that the designed algorithm can achieve a mean average precision of 75.2% on NWPU VHR-10 dataset, with 181, 5.2, 26.3, and 15 frames per second on the above four typical hardware platforms, respectively, which finally demonstrate the effectiveness and high accuracy of the proposed algorithm.

The skylight polarization pattern, which is a result of the scattering of unpolarized sunlight by particles in the atmosphere, can be used by many insects for navigation. Inspired by insects, several polarization navigation sensors have been designed and combined with various heading determination methods in recent years. However, up until now, few of these studies have fully considered the influences of different meteorological conditions, which play key roles in navigation accuracy, especially in cloudy weather. Therefore, this study makes a major contribution to the study on bio-inspired heading determination by designing a skylight compass method to suppress cloud disturbances. The proposed method transforms the heading determination problem into a binary classification problem by segmentation, connected component detection, and inversion. Considering the influences of noise and meteorological conditions, the binary classification problem is solved by the soft-margin support vector machine. In addition, to verify this method, a pixelated polarization compass platform is constructed that can take polarization images at four different orientations simultaneously in real time. Finally, field experimental results show that the designed method can more effectively suppress the interference of clouds compared with other methods.

Atmospheric polarization patterns are induced by scattering of unpolarized sunlight in the atmosphere and many insects can detect them by compound eyes to derive compass information. Several kinds of polarization navigation algorithms have been designed based on the working principles of the polarization vision of insects. In view of the shortcomings of the existing polarization navigation algorithms, a polarization orientation determination algorithm based on the extremum of moment of inertia is proposed. This algorithm detects the global symmetry of atmospheric polarization patterns of the whole sky to obtain the azimuth angle, so it is reliable and has strong anti-interference ability. The experimental results show that the polarization orientation determination algorithm is not sensitive to the interference of the thin clouds in the sky.

The rapid development of unmanned aerial vehicle (UAV) has made great progress for its widespread uses in military and civilian applications in recent years. One of the most important issues related to the UAV application is UAV path planning especially in harsh environment. In order to find the optimal route as well as effectively avoid three-dimensional obstacles, the performance of UAV path planning algorithm is essential. This paper proposes an improved three-dimensional A∗ algorithm which is based on Digital Elevation Map (DEM) to obtain the initial path and combines Bresenham line-drawing algorithm to line the initial path. Finally Bezier is used curve to smooth the lining path. Above all, can get three-dimensional smooth path that satisfy the fixed wing UAVs security and kinematic constraints. The simulation results have shown that the path planning algorithm is effective in fixed wing UAV three-dimensional path planning. Based on the attitude sensor, a real-time three-dimensional navigation scene simulation system is constructed, which can effectively show the process of three-dimensional navigation and obstacle avoidance.

This paper investigates a fast and reliable method to optimize the terminal trajectory with multi-constraints for morphing-wing missile to enlarge the maximum final velocity, and ascertains the performance on increasing the terminal velocity of morphing wing missile in comparison with fixed wing missile. The effect of morphing-wing missile with flying and varying parameters on aerodynamic characteristics is analyzed under the steady and unsteady condition respectively, and trajectory optimization model for maximum final velocity is established with multi-constraints. Then a solving strategy for trajectory optimization is proposed and described in detail based on hp-adaptive pseudo-spectral method. Finally, the terminal trajectory optimization simulation is performed with the presented optimal method. The simulation results suggest that it is feasible for the given morphing wing missile via changing the swept angle to increase final velocity by 13.8% over with the fixed wing missile, the effectiveness of the proposed method is confirmed on solving the optimal control problem with multiple constraints.

Irregular driving is hazard driving behaviours to transport safety. Therefore, it is crucial to detect the irregular driving in the early stage for preventing the accidents happen. In this work, a fuzzy logic based irregular driving detection model is proposed. Fuzzy logic concepts can be applied to a problem based on linguistic rules, which are determined by problem domain experts. Sensitivity analysis has been conducted to analyse the relative parameters in the system. Simulation results have demonstrated the effectiveness of the model in terms of timeliness, availability and recall for the irregular driving detection.

In this paper, a new approach to gain-scheduling of robust autopilot was proposed for morphing guided aerial bombs in a bank-to-turn (BTT) mode. The nonlinear dynamic model was established and anticipant performance criterions were given firstly. Feedback robust BTT autopilot of 3-channel was designed independently for selected operating points in order to restrain cross-channel coupling disturbances and aerodynamic perturbations. Then, the sweepback and mach number were selected to schedule the control gains iteratively based on guardian maps for ensuring all closed-loop poles to locate inside the desired region. The method proposed here attempted to extend the performance of an initial design obtained for a single arbitrary point to the whole linearized domain while maintaining expected stability over the entire range of sweepback. We carried out a number of time- and frequency-domain analysis procedures on the resulting designs and assessed the performance on a nonlinear simulation. The numerical simulations showed that the method proposed applied on the autopilot design and had better tracking performance.

The aim of this paper is to explore the possibility of using geo-referenced aerial images to augment an Unmanned Aerial Vehicle (UAV) navigation system in case of GPS failure. The proposed method is based on image matching between the current view from a monocular video camera and a previously known database of geo-referenced images. Only natural landmarks provided by a feature tracking algorithm will be considered, without the help of visual beacons or artificial landmarks. The novel idea is to perform global localization, position tracking, localization failure recovery and safe landing. Key points of current video frame and reference database are extracted by implementing Speeded-Up Robust Features (SURF) algorithm on a NVIDIA Geforce 240M graphics processing unit (GPU). The proposed system has been tested on real flight data and testing results confirmed that the proposed system is real-time and sufficiently robust to scale, rotation, illumination, noise and affine projection, thus it can be an ideal scheme for autonomous navigation system for UAVs under GPS blockage conditions.

In order to solve the problem that the precision and stability of Doppler radar/Fiber Optical Gyroscope Strapdown Inertial Naivgation System (FOG-SINS)/Barometer Integrated Navigation System (DFBINS) for helicopters will be highly affected if there are outliers in doppler radar, especially consecutive outliers appear, a useful method to eliminate these outliers based on orthogonality of innovation is proposed in this paper. Outliers in Doppler radar can be detected by judging whether the orthogonality of innovation in Kalman filter is lost or not, and an activation function as the weight factor to each element of observation is assigned, so it can keep innovation sequence of kalman filter orthogonal and outliers can be detected and corrected. The excellent results of digital simulation for a long distance straight line flying test show that the modified method is effectively resistant to the adverse effects on accuracy and stability of DFBINS caused by outliers in Doppler radar, the proposed algorithm is of high value in practice.

In order to meet the requirements of high precision orbit determination and prediction for the safe and pinpoint lunar soft landing, the paper presents an autonomous navigation based on SINS (strapdown inertial navigation system) /DOPPLER Radar/Radar Altimeter integrated navigation system. According to the error characteristics of each sensor, an effective information fusion algorithm is designed. The excellent result of vehicle experiment indicates that the integrated navigation has high precision, the system design method is successful, and it has better value of engineering application.

Though fiber optic gyroscope strapdown inertial navigation system (FOG-SINS)/Doppler radar/ Baro-altimeter integrated navigation system has been done some research in theory, the appropriate study of practice has not been done yet in China and there are still many problems need to be solved. This paper gives a new design of a low cost FOG-SINS/Doppler radar /Baro-altimeter integrated navigation system according to the disadvantages of precision and independence of navigation for helicopters. The mathematical model and digital simulation were presented and a series of long endurance hardware-in-the-loop simulation tests were conducted in a land vehicle. The excellent result of the simulation tests indicates that the designed system is effective, it can satisfy the requirement of navigation for helicopters when the GPS is disabled.

Recent years have seen a booming of safety-related Intelligent Transportation System (ITS) applications, which have placed increasingly stringent requirements on the performance of Global Navigation Satellite Systems (GNSS). Examples include lane control, collision avoidance, and intelligent speed assistance. Detecting the lane level anomalous driving behavior is crucial for these safety critical ITS applications. The two major issues associated with the lane-level irregular driving identification are (1) accessibility to high accuracy positioning and vehicle dynamic parameters, and (2) extraction of anomalous driving behavior from these parameters. This paper introduces an integrated algorithm for detecting lane-level anomalous driving. Lane-level high accuracy vehicle positioning is achieved by fusing GPS and Beidou feeds with Inertial Measurement Unit (IMU) using Unscented Particle Filter (UPF). Anomalous driving detection is achieved based on the application of a newly designed Fuzzy Inference System. Computer simulation and real-world field test demonstrate the advantage of the proposed approach over existing ones from previous studies.