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The algorithm proposed in this paper aims at solving the problem of star map matching in high-limiting-magnitude astronomical images, which is inspired by geometric voting star identification techniques. It is a two-step star map matching algorithm relying only on angular features, and adopts a reasonable matching strategy to overcome the problem of poor real-time performance of the geometric voting algorithm when the number of stars is large. The algorithm focuses on application scenarios where there are a large number of dense stars (limiting magnitude greater than 13, average number of stars per square degree greater than 185) in the image, which is different from the sparse star identification problem of the star tracker, which is more challenging for the robustness and real-time performance of the algorithm. The proposed algorithm can be adapted to application scenarios such as unreliable brightness information, centroid positioning error, visual axis pointing deviation, and a large number of false stars, with high accuracy, robustness, and good real-time performance. [ABSTRACT FROM AUTHOR]

Observation task planning is a key issue and the first step in the development of automated Satellite Laser Ranging (SLR) systems. Aiming at the problem of dynamic change of cloud cover during SLR operation, this paper proposes an autonomous mission planning algorithm for SLR based on the Rolling Horizon Optimization (RHO) framework. A hybrid event- and cycle-driven replanning mechanism is adopted, and four functional modules, rolling, planning, information acquisition and decision-making, are established to decompose the SLR observation task planning process into a series of static planning intervals. An improved ant colony algorithm is proposed and utilized to realize the autonomous planning of SLR system observation tasks, and the above autonomous planning algorithm is verified and analyzed based on the SLR system at station 7237. The results show that the above algorithm can effectively increase the number of observation satellites and revenue under cloud disturbance, solve the problems of low efficiency and poor interference resistance of conventional static algorithms, and provide a new research idea for the establishment of an unattended SLR system. [ABSTRACT FROM AUTHOR]

Star identification plays a key role in spacecraft attitude measurement. Currently, most star identification algorithms tend to perform well only in a scene without noise and are highly sensitive to noise. To solve this problem, this paper proposes a star identification algorithm based on the maximum spanning tree (MST) index and multi-order continuous cycle angle (CCA) intended for the lost-in-space mode. In addition, a neighboring star selection method named dynamic eight-quadrant (DEQ) is developed. First, the DEQ method is used to select high-confidence neighboring stars for the main star. Then, the star image is regarded as a graph, and the Prim algorithm is employed to construct the MST pattern for each guide star, which is then combined with the K vector index to perform the main star candidate search. Finally, the Jackard similarity voting for the multi-order CCA of the main star is used to identify the main star, and the global neighboring star identification is conducted by the multi-order CCA of neighboring stars. The simulated and real star images test results show that compared with five mainstream algorithms, when the position noise is 1 pixel, the number of false stars is five, the magnitude noise is 0.5, and the identification accuracy of the proposed algorithm is higher than 98.5%. Therefore, the proposed algorithm has excellent anti-noise ability in comparison to other algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a high-altitude platform (HAP) and unmanned aerial vehicles (UAVs) collaboration framework in non-orthogonal multiple access (NOMA)-enabled Internet of Things (IoT) networks with the presence of an eavesdropping UAV. For the considered framework, we investigate the uplink secure transmission by optimizing channel allocation from UAVs to HAP, users' power, and UAVs' three-dimensional (3D) position. To solve this non-convex problem, we adopt the K-means cluster pair algorithm to divide paired users into different groups and each cluster can be served by a corresponding UAV. Then, the formulated optimization problem is decoupled into three subproblems and tackled iteratively based on the block coordinate descent (BCD) algorithm. Finally, simulation results verify that the proposed network architecture can achieve a higher secure rate, faster convergence evolution, and lower complexity in comparison with the current works. [ABSTRACT FROM AUTHOR]

When the transmitter is in motion, the airborne passive bistatic radar (PBR) has a complex clutter geometry and lacks independent and identically distributed training samples in clutter estimation and suppression. In order to solve these problems, this paper proposes a space–time adaptive processing (STAP) algorithm based on root off-grid sparse Bayesian learning. The proposed algorithm first models the space–time base of the dictionary as an adjustable state. Then, the positions of those dynamic bases are optimized by iterating a maximum expectation algorithm. In this way, the off-grid error in clutter estimation can be eliminated even when the modeling grid is wide. To further improve the accuracy of clutter estimation, the proposed algorithm eliminates the error caused by samples with singular values in the root off-grid sparse Bayes learning by artificially adding pseudorandom noise and using hypothesis testing. The simulation results show that the proposed algorithm achieves better performance than the existing algorithms. [ABSTRACT FROM AUTHOR]

Remote sensing is the primary way to extract the impervious surface areas (ISAs). However, the obstruction of vegetation is a long-standing challenge that prevents the accurate extraction of urban ISAs. Currently, there are no general and systematic methods to solve the problem. In this paper, we present a morphological feature-oriented algorithm, which can make use of the OSM road network information to remove the obscuring effects when the ISAs are extracted. Very high resolution (VHR) images of Wuhan, China, were used in experiments to verify the effectiveness of the proposed algorithm. Experimental results show that the proposed algorithm can improve the accuracy and completeness of ISA extraction by our previous deep learning-based algorithm. In the proposed algorithm, the overall accuracy (OA) is 86.64%. The results show that the proposed algorithm is feasible and can extract the vegetation-obscured ISAs effectively and precisely. [ABSTRACT FROM AUTHOR]

Dimensionality reduction based on random projection (RP) includes two problems, namely, the dimensionality is limited by the data size and the class separability of the dimensionality reduction results is unstable due to the randomly generated projection matrix. These problems make the RP algorithm unsuitable for large-size hyperspectral image (HSI) classification. To solve these problems, this paper presents a new partitioned RP (PRP) algorithm and proves its rationality in theory. First, a large-size HSI is evenly divided into multiple small-size sub-HSIs. Afterwards, the projection matrix that maximizes the class separability is selected from multiple samplings in which the class dissimilarity measurement is defined as large inter-class distance and small intra-class variance. By using the same projection matrix, each small-size sub-HSI is projected to generate a low dimensional sub-HSI, thereby generating a low dimensional HSI. Next, the minimum distance (MD) classifier is utilized to classify the low dimensional HSI obtained by the PRP algorithm. Finally, four real HSIs are used for experiments, and three of the most popular classification algorithms based on RP are selected as comparison algorithms to validate the effectiveness of the proposed algorithm. The classification performance is evaluated with the kappa coefficient, overall accuracy (OA), average accuracy (AA), average precision rate (APR), and running time. Experimental results indicate that the proposed algorithm can obtain reliable classification results in a very short time. [ABSTRACT FROM AUTHOR]

In the South Atlantic Anomaly (SAA) area, due to the influence of transient noise caused by space radiation, a star sensor can easily stay in the lost-in-space (LIS) mode for a long time. To solve this problem, this paper proposes a fast star identification (FSI) algorithm. First, a noise suppression method based on scale assessment and neighborhood comparison is developed. Next, a fast and accurate search technique of multiple main stars based on the k-vector technique is used to realize star identification. The search technique builds a self-defined attribute database of stars, and a fast search method of a repeated star identity is proposed to realize the positioning of the main star. Lastly, the final main stars are obtained through the comparison of field of view and verification of angular distance. The experimental results showed that when the star sensor works at a speed of 0.1°/s and the level of transient noise signals is lower than 900, the successful identification rate is higher than 70%. In addition, compared with the triangle algorithm, match group algorithm, and multi-pole algorithm (MPA), the proposed FSI algorithm has the advantages of a higher successful identification rate and a faster execution speed. [ABSTRACT FROM AUTHOR]

With the development of information technology in modern military confrontation, specific emitter identification has become a hot and difficult topic in the field of electronic warfare, especially in the field of electronic reconnaissance. Specific emitter identification requires a historical reconnaissance signal as the matching template. In order to avoid being intercepted by enemy electronic reconnaissance equipment, modern radar often has multiple sets of working parameters, such as pulse width and signal bandwidth, which change when performing different tasks and training. At this time, the collected fingerprint features cannot fully match the fingerprint template in the radar database, making the traditional specific emitter identification algorithm ineffective. Therefore, when the working parameters of enemy radar change, that is, when there is no such variable working parameter signal template in our radar database, it is a bottleneck problem in the current electronic reconnaissance field to realize the specific emitter identification. In order to solve this problem, this paper proposes a network model based on metric learning. By learning deep fingerprint features and learning a deep nonlinear metric between different sample signals, the same individual sample signals under different working parameters can be associated. Even if there are no samples under a certain kind of working parameter signal, it can still be associated with the original individual through this network model, so as to achieve the purpose of specific emitter identification. As opposed to the situation in which the traditional specific emitter identification algorithm cannot be associated with the original individual when the signal samples of changing working parameters are not collected, the algorithm proposed in this paper can better solve the problem of changing working parameters and zero samples. [ABSTRACT FROM AUTHOR]

Video synthetic aperture radar (ViSAR) can provide long-time surveillance of a region of interest (ROI), which is one of the hotspot directions in the SAR field. In order to better display ViSAR, a high resolution and high frame rate are needed. Azimuth integration angle and sub-aperture overlapping ratio, which determine the image resolution and frame rate, respectively, are analyzed in depth in this paper. For SAR imaging algorithm, polar format algorithm (PFA) is applied, which not only has high efficiency but is also easier to integrate with autofocus algorithms. Due to sensitivity to motion error, it is very difficult to obtain satisfactory focus quality, especially for SAR systems with a high carrier frequency. The three-step motion compensation (MOCO) proposed in this paper, which combines GPS-based MOCO, map-drift (MD) and phase gradient autofocus (PGA), can effectively compensate for motion error, especially for short wavelengths. In ViSAR, problems such as jitter, non-uniform grey scale and low image signal noise ratio (SNR) between different aspects images also need to be considered, so a ViSAR generation method is proposed to solve the above problems. Finally, the results of ViSAR in THz and Ku band demonstrate the effectiveness and practicability of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the joint design of a transmit beampattern and angular waveform (AW) for colocated multiple-input multiple-output (MIMO) radars. The importance of the AW in the proposed signal processing strategy is first clarified, and then, two optimization models are established, which are aimed at either the power spectral density (PSD) design or the spectral compatibility and similarity design of the AW. There are two main differences between the proposed models and existing models. First, instead of matching a desired template or maximizing the transmit power on specific regions, the transmit beampattern in this paper is optimized to approach several key points, which guarantees the high transmit gain and the flexible adjustment of each beam gain. Second, instead of optimizing the performance of the transmit waveform, only the characteristics of the AW are examined, and they can be constrained quantitatively according to their relationship with the transmit gain. The two models can be unified into the same framework, and an efficient algorithm is proposed to solve the problem under a constant modulus constraint. The convergence of the proposed algorithm is demonstrated, and some improvements to reduce the computational complexity are proposed. Numerical simulations showed that compared to the existing methods, the proposed approach can be used to obtain a higher transmit gain, flexibly adjust each beam gain, and more accurately control the PSD, spectral compatibility, and similarity of the AW. Moreover, numerical simulations showed that, compared to the use of existing methods, the proposed algorithm has higher computational efficiency. [ABSTRACT FROM AUTHOR]

As an emerging orbital system with flexibility and brand application prospects, the highly elliptical orbit synthetic aperture radar (HEO SAR) can achieve both a low orbit detailed survey and continuous earth surface observation in high orbit, which could be applied to marine reconnaissance and surveillance. However, due to its large eccentricity, two challenges have been faced in the signal processing of HEO SAR at present. The first challenge is that the traditional equivalent squint range model (ESRM) fails to accurately describe the entire range for the whole orbit period including the perigee, the apogee, and the squint subduction section. The second one is to exploit an efficient HEO SAR imaging algorithm in the squinted case which solves the problem that traditional imaging algorithm fails to achieve the focused imaging processing of HEO SAR during the entire orbit period. In this paper, a novel imaging algorithm for HEO SAR is presented. Firstly, the signal model based on the geometric configuration of the large elliptical orbit is established and the Doppler parameter characteristics of SAR are analyzed. Secondly, due to the particularity of Doppler parameters variation in the whole period of HEO, the equivalent velocity and equivalent squint angle used in MESRM can no longer be applied, a refined fourth-order equivalent squint range model(R4-ESRM) that is suitable for HEO SAR is developed by introducing fourth-order Doppler parameter into Modified ESRM (MESRM), which accurately reconstructs the range history of HEO SAR. Finally, a novel imaging algorithm combining azimuth resampling and time-frequency domain hybrid correlation based on R4-ESRM is derived. Simulation is performed to demonstrate the feasibility and validity of the presented algorithm and range model, showing that it achieves the precise phase compensation and well focusing. [ABSTRACT FROM AUTHOR]

How to accurately detect small targets from the complex maritime environment has been a bottleneck problem. The strong wind-wave backlight conditions (SWWBC) is the most common situation in the process of distress target detection. In order to solve this problem, the main contribution of this paper is to propose a small target detection method suitable for SWWBC. First of all, for the purpose of suppressing the gray value of the background, it is analyzed that some minimum points with the lowest gray value tend to gather in the interior of the small target. As the distance from the extreme point increases, the gray value of the pixel in all directions also increases by the same extent. Therefore, an inverse Gaussian difference (IGD) preprocessing method similar to the distribution of the target pixel value is proposed to suppress the uniform sea wave and intensity of the sky background. So as to achieve the purpose of background suppression. Secondly, according to the feature that the small target tends to "ellipse shape" in both horizontal and vertical directions, a multi-scale and multi-directional Gabor filter is applied to filter out interference without "ellipse shape". Combined with the inter-scale difference (IsD) operation and iterative normalization operator to process the results of the same direction under different scales, it can further suppress the noise interference, highlight the significance of the target, and fuse the processing results to enrich the target information. Then, according to different texture feature distributions of the target and noise in the multi-scale feature fusion results, a cross-correlation (CC) algorithm is proposed to eliminate noise. Finally, according to the dispersion of the number of extreme points and the significance of the intensity of the small target compared with the sea wave and sky noise, a new peak significance remeasurement method is proposed to highlight the intensity of the target and combined with a binary method to achieve accurate target segmentation. In order to better evaluate the performance index of the proposed method, compared with current state-of-art maritime target detection technologies. The experimental results of multiple image sequence sets confirm that the proposed method has higher accuracy, lower false alarm rate, lower complexity, and higher stability. [ABSTRACT FROM AUTHOR]

The recently developed gauge-radar-satellite merged hourly precipitation dataset (CMPAS-NRT) offers broad applications in scientific research and operations, such as intelligent grid forecasting, meteorological disaster monitoring and warning, and numerical model testing and evaluation. In this paper, we take a super-long Meiyu precipitation process experienced in the Yangtze River basin in the summer of 2020 as the research object, and evaluate the monitoring capability of the CMPAS-NRT for the process from multiple perspectives, such as error indicators, precipitation characteristics, and daily variability in different rainfall areas, using dense surface rain-gauge observation data as a reference. The results show that the error indicators for CMPAS-NRT are in good agreement with the gauge observations. The CMPAS-NRT can accurately reflect the evolution of precipitation during the whole rainy season, and can accurately capture the spatial distribution of rainbands, but there is an underestimation of extreme precipitation. At the same time, the CMPAS-NRT product features the phenomenon of overestimation of precipitation at the level of light rain. In terms of daily variation of precipitation, the precipitation amount, frequency, and intensity are basically consistent with the observations, except that there is a lag in the peak frequency of precipitation, and the frequency of precipitation at night is less than observed, and the intensity of precipitation is higher than observed. Overall, the CMPAS-NRT product can successfully reflect the precipitation characteristics of this super-heavy Meiyu precipitation event, and has a high potential hydrological utilization value. However, further improvement of the precipitation algorithm is needed to solve the problems of overestimation of light rainfall and underestimation of extreme precipitation in order to provide more accurate hourly precipitation monitoring dataset. [ABSTRACT FROM AUTHOR]

Due to the high temporal repetition rates, median/low spatial resolution remote sensing images are the main data source of change detection (CD). It is worth noting that they contain a large number of mixed pixels, which makes adequately capturing the details in the resulting thematic map challenging. The spectral unmixing (SU) method is a potential solution to this problem, as it decomposes mixed pixels into a set of fractions of the land covers. However, there are accumulated errors in the fractional difference images, which lead to a poor change detection results. Meanwhile, the spectra variation of the endmember and the heterogeneity of the land cover materials cannot be fully considered in the traditional framework. In order to solve this problem, a novel change detection approach with image stacking and dividing based on spectral unmixing while considering the variability of endmembers (CD_SDSUVE) was proposed in this paper. Firstly, the remote sensing images at different times were stacked into a unified framework. After that, several patch images were produced by dividing the stacked images so that the similar endmembers according to each land cover can be completely extracted and compared. Finally, the multiple endmember spectral mixture analysis (MESMA) is performed, and the abundant images were combined to produce the entire change detection thematic map. This proposed algorithm was implemented and compared to four relevant state-of-the-art methods on three experimental data, whereby the results confirmed that it effectively improved the accuracy. In the simulated data, the overall accuracy (OA) and Kappa coefficient values were 99.61% and 0.99. In the two real data, the maximum of OA were acquired with 93.26% and 80.85%, which gained 14.88% and 13.42% over the worst results at most. Meanwhile, the Kappa coefficient value was consistent with the OA. [ABSTRACT FROM AUTHOR]