Synthetic aperture radar (SAR) automatic target recognition (ATR) has been widely used by scholars around the world and achieved excellent results. However, occluded SAR target recognition is still a very challenging task. In this letter, we propose a center local constraint shadow residual network (ClcsrNet) for occluded SAR target recognition. First, the shadow features of SAR images are extracted to improve the robustness of the network to occlusion. Then, the shadow features, the target convolutional features, and the residual features are fused to increase the feature diversity of the network. Finally, we combine the center loss and the local constraint loss to optimize the network. The center loss is used to better cluster the targets in the same class. The local constraint loss is used to maintain the local structure of the target, which increases the separability between different classes. Experiments on the moving and stationary target acquisition and recognition (MSTAR) datasets demonstrate that the proposed ClcsrNet can achieve higher accuracy and better robustness than the comparison algorithms in occluded SAR target recognition.

The detection of small floating targets is a challenging problem for maritime surveillance radar. To achieve effective detection within complex sea clutter background, an innovative graph feature detector is proposed in this letter. First, the received radar sequences are converted into graphs to capture the correlation of signals. Then, three graph features weight peak height (WPH), graph complexity (GC), and graph entropy (GE) of weighted difference visibility graph (WDVG) are proposed. The topological properties of the WDVGs constructed from the phase domain of radar echoes is analyzed, which provides insights into the underlying dynamics structures of the observed phenomena. In the detection part, an improved false alarm rate controllable (FAC) concave detector is designed, which is based on the concave hull-learning algorithm. Experiments results based on the real measured IPIX radar datasets confirm that the proposed method has a better performance compared with the existing feature-based methods, especially under shorter observation time (0.128 s).

Infrared (IR) search and track systems are widely applied in aerospace and defense fields. Infrared small target detection (IRSTD) in heavy clouds and chaotic terrestrial environments remains a challenging task. The semantic features of IR small targets are highly prone to vanishing with the addition of network layers. Transformer with quadratic computational complexity struggles for local feature refinement. To tackle this issue, we introduce a Mamba-driven approach dubbed Spatial- Spectral Mamba Interactive Learning (SMILE) network. Specifically, the perspective transformation structures heterogeneous backgrounds. The reconstructed data couples Mamba’s flattened multidirectional scanning mechanism. Given that small targets possess sparse and high-frequency properties, spatial Mamba and spectral Mamba collaboratively enrich the semantic features of small targets. The Dual-Path Aggregation (DPA) network is engineered to integrate the saliency and high-frequency attributes of small targets, effectively balancing detailed and contextual features without overwhelming the network. The Hybrid Representation Learning Module (HRLM) refines the local features to inscribe the intact edge structure. Both qualitative and quantitative experiments demonstrate that our proposed SMILE outperforms 14 recent benchmark algorithms on two public datasets.

Infrared (IR) search and track systems are widely applied in aerospace and defense fields. Infrared small target detection (IRSTD) in heavy clouds and chaotic terrestrial environments remains a challenging task. The semantic features of IR small targets are highly prone to vanishing with the addition of network layers. Transformer with quadratic computational complexity struggles for local feature refinement. To tackle this issue, we introduce a Mamba-driven approach dubbed Frequency-Spatial Interaction Reinforcement Network (FIRE-Net). Specifically, the perspective transformation structures heterogeneous backgrounds. The reconstructed data couples Mamba’s flattened multidirectional scanning mechanism. Given that small targets possess sparse and high-frequency properties, spatial Mamba and frequency Mamba collaboratively enrich the semantic features of small targets. The Texture Enhancement Module (TEM) effectively fuses spatial and frequency features to enhance the contrast information of small targets. To refine the features, the Fine-Grained Reinforcement Module (FRM) integrates multiple gradient operators to inscribe the intact small target profile. Both qualitative and quantitative experiments demonstrate that our proposed FIRE-Net outperforms 14 recent benchmark algorithms on multiple public datasets.

Remote sensing image restoration, which aims to reconstruct corrupted or missing regions, heavily relies on low-rank models. A recent trend in this field is to jointly model low-rank and local smoothness priors using a single regularization term, in order to better recover fine textures. However, due to the entanglement of low- and high-frequency components in an image, existing methods often struggle to simultaneously capture both coarse-grained structures and fine-grained textures, while also suffering from high computational complexity. To address these issues, this paper proposes a novel regularization, the Haar Nuclear Norm (HNN), for efficient and effective remote sensing image restoration. HNN transforms images into wavelet coefficients that separate low-frequency (coarse-grained) and high-frequency (fine-grained) components, and enforces low-rankness via nuclear norms on the mode-3 unfolding matrices of these wavelet coefficients. Experimental evaluations conducted on hyperspectral image inpainting, multi-temporal image cloud removal, and hyperspectral image denoising have revealed the HNN's potential. Typically, HNN achieves a performance improvement of 1-4 dB and a speedup of 10-28x compared to some state-of-the-art methods (e.g., tensor correlated total variation, and fully-connected tensor network) for inpainting tasks. The code is available at https://github.com/isyuchang/HNN.

Aiming at achieving efficient and high-precision imaging in complex radar imaging modes, a fast BP imaging algorithm using subaperture grouping is proposed in this paper. The subapertures within each group are fused by the wavenumber spectrum under the same coordinate system, and the groups are fused recursively by the fast BP imaging algorithm. The limitation of the subaperture bandwidth when using a unified coordinate system for FFBP imaging is derived and analyzed. The proposed algorithm can achieve satisfying imaging performance without subaperture overlap, and improves the accuracy and efficiency by reducing interpolation operations of the FFBP algorithm. The effectiveness of the proposed algorithm is verified on simulated and real measured SAF data.

In the area of vehicular ad hoc networks (VANETs), efficient data transmission stands as a cornerstone for ensuring dependable communication. This paper focuses on the key role of optimizing antenna configuration concerning data volumes to improve communication efficiency within VANETs. To attain efficient data transmission and improve user experience, we analyze and compare the influence of varied data volumes and antenna settings on communication quality stemming from bit error rate performance. Through experimental validation across diverse signal detection scenarios, disparities in performance across different antenna configurations are confirmed, thereby furnishing invaluable insights into the efficacy of dynamically selecting antenna configurations based on data volumes. In essence, this paper aims to demonstrate the intricacies of optimizing antenna settings to adaptly navigate fluctuations in transmitted data volumes, thereby increasing communication efficiency and ensuring robust communication in VANETs.

Convolutional neural networks (CNNs) have been widely employed in synthetic aperture radar (SAR) target recognition due to their powerful feature extraction capability. However, the performance of CNN-based SAR target recognition algorithms is often affected by imbalanced datasets, in which some classes own plenty of samples and some classes own few samples. To address this issue, this letter proposes a large-margin Gaussian mixture with a ring loss network (LGM-RNet). To improve CNN’s recognition performance for classes with few samples, the algorithm clusters features of each class in the feature space and makes all the data to be equally distributed on a circle. Furthermore, to mitigate the impact of speckle noise in SAR images on target recognition, a denoising method based on Euclidean loss and the total variation loss is introduced. The proposed algorithm aims to improve the accuracy and robustness of imbalanced SAR image target recognition. Experimental results have verified the effectiveness of the proposed algorithm.

Infrared (IR) search and track system are widely used in aerospace and defense fields. IR small target detection (IRSTD) in heavy clouds and chaotic terrestrial environments remains a challenging task. The primary reason is the interference of clutter in IR images and a lack of complete excavation of spatial and structural information from small targets. To accommodate heterogeneous backgrounds and enhance the feature recognition of small targets, this article introduces a model-driven approach called the visual attention-guided sparse reconstruction (VAG-SR) model. Specifically, the construction of holistic spatiotemporal data structures maintains full internal correlations to compensate for a lack of texture knowledge. Furthermore, based on the fact that moving targets in sequences receive more attention, we introduce an attention map for small targets in the subspace analysis framework. The attention map facilitates the sparse subspace to concentrate on small target areas to mitigate interference from clutter and noise. The proposed objective function systematically considers the sparsity, saliency, and aggregability of small targets. Finally, a closed-form solution to the optimization algorithm is derived to solve the proposed model. Both qualitative and quantitative experiments on diverse and real scenes demonstrate the superiority of the VAG-SR model over 12 baseline methods.

Synthetic aperture radar (SAR) is a critical imaging technique that is widely used for civil and military tasks, as it is featured with an excellent ability for high resolution imaging. However, due to the severe shortage of SAR images, the performance of automatic target recognition (ATR) is greatly sabotaged. Generative adversarial network (GAN) is often applied for data augmentation of small-sized dataset. In this paper, based on auxiliary classifier GAN (ACGAN) and top-k training technique, we propose double top-k training, which implements a modification during training without any further adjustment on model architecture. The proposed method is to enforce generator to only optimize on generated images that perform well in both discriminator and auxiliary classifier, and discard images of poor performance. We evaluate the generated images via recognition on the moving and stationary target acquisition and recognition (MSTAR) dataset. Recognition accuracy and Fréchet inception distance (FID) score indicate better generation results of the proposed method compared with original ACGAN.

Global Navigation Satellite Systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation System (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance.

Superpixel can maintain the boundary of the target and reduce the influence of speckle noise, which has been widely applied to synthetic aperture radar (SAR) image target detection. But the size of the superpixel has a great impact on the performance of superpixel-based SAR target detection algorithms. To solve this problem, we propose a multi-level ship target detection algorithm based on superpixel segmentation. Firstly, the SAR images are segmented in different levels with different superpixel sizes. Different descriptions of the SAR images are obtained in different levels. Secondly, we determine the feature of the superpixels in each level. And in order to enhance the adaptability of the proposed algorithm, we propose an adaptive distance calculation method to select the contrast superpixels in each level. Thirdly, the soft detection results are realized in each level by using the fuzzy C-means (FCM) algorithm. At last, the soft detection results obtained in different levels are fused by a new fusion strategy to achieve the final ship target detection result. The influences caused by different superxiel sizes can be effectively eased by fusion. Experiments in different SAR images have verified the effectiveness of the proposed algorithm in accurately detecting ship targets and insensitivity to the superpixel size.

Maritime target detection is one of the most complicated problems in the radar signal processing field. Since traditional constant false alarm rate detection methods rely on the clutter distribution model, the mismatch of the sea clutter model leads to a decrease in the target detection performance. In this paper, the amplitude statistical entropy (ASE) of sea clutter sequence is extracted as a feature to describe the degree of aggregation of the sea clutter amplitude statistical histogram. Then a novel target detection algorithm based on ASE is proposed, which is not affected by the degree of the model matching between sea clutter datasets and statistical model. Finally, the experiment result based on the Canadian IPIX radar datasets confirms the effectiveness of this method.

Space-based radar has the characteristics of all-weather operation, and can accurately provide important data for understanding global environmental changes. On the other hand, with the rapid development of radio technology, space-based radar is facing more and more interference, such as terrestrial interference and inter-satellite interference, which greatly distort the measurements and degrade the image quality. In this paper, a novel interference mitigation method based on multi-temporal coupling analysis is proposed. The temporal-spatial coupling between time-series images could be modeled as low rank, while the interference follows the sparsity constraints due to the time-varying property. The interference extraction and mitigation on remote sensing images is realized by optimization by joint low-rank and sparsity regularization. The experimental results of Sentinel-1A data show that the method can achieve the separation of interference and restore clear remote sensing images with little distortion.

In this letter, a small target detection method in sea clutter background is proposed based on multiview polarization features and localized one-class support vector machine (LOCSVM). In the proposed method, three views of features that represent the divergence between the small target and the clutter are extracted: 1) the study of the target scattering characteristics shows that the dominant scattering components of the small target cell (TC) are sphere scattering and diplane scattering, while the scattering mechanism of the clutter cell is varied and affected by the sea conditions. Therefore, the relative powers of the sphere and diplane scattering components are extracted based on the polarimetric scattering matrix; 2) Because the target is a definite shaped object, its cell does not exist in the fractal characteristics, while the clutter cell is on the contrary. Therefore, the average Tsallis entropy (TE) is extracted from full-polarization channels to reflect the fractal characteristics of the cell; and 3) As the generalization of the Doppler entropy, because the frequency spectrum of the TC is more congregate, the TE can reflect the Doppler characteristics. Then, in the detection stage, a novel one-class classifier, referred to as LOCSVM, is designed as the detector. LOCSVM is a combination of$k$-means clustering and OCSVM and can enhance the detection performance of OCSVM. The experimental results on the IPIX data show the effectiveness of the proposed method, especially in the case of short observation time.

Agent-based simulation is a useful approach for the analysis of dynamic population evolution. In this field, the existing models mostly treat the migration behavior as a result of utility maximization, which partially ignores the endogenous mechanisms of human decision making. To simulate such a process, this article proposes a new cognitive architecture called the two-layered integrated decision cycle (TiDEC) which characterizes the individual's decision-making process. Different from the previous ones, the new hybrid architecture incorporates deep neural networks for its perception and implicit knowledge learning. The proposed model is applied in China and U.S. population evolution. To the best of our knowledge, this is the first time that the cognitive computation is used in such a field. Computational experiments using the actual census data indicate that the cognitive model, compared with the traditional utility maximization methods, cannot only reconstruct the historical demographic features but also achieve better prediction of future evolutionary dynamics.

Focusing on the problem of target recognition in complex land backgrounds, a target identification method using scanning correlation and beam pointing is proposed in this paper. Considering the complex scattering characteristics of ground clutters, correlation thresholds associated with range and angles are set to eliminate false targets. Firstly, seeker scanning is implemented twice from left to right, then from right to left to eliminate the negative influences of ground clutters. The information of the detected targets during scanning including range and angles are stored. Point or body identification is realized based on the high-resolution range profile (HRRP). Finally, error distribution area is calculated by combining with the beam pointing information from the fire control system. Target identification is realized by finding the nearest target.

Synthetic aperture radar (SAR) target configuration recognition is a challenging task, and the key point is to realize effective feature extraction. An algorithm combing the advantages of sparse graph and locality preserving projections (LPP) is proposed to achieve SAR target configuration recognition. Taking the merits of sparse representation (SR) into consideration, an affinity matrix is established to realize effective structure preserving of the dataset. Besides, the problem of matrix singularity in LPP is effectively resolved by diagonal loading. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) database validate the effectiveness and superiority of the proposed algorithm.

Focusing on the problem of the real-time implementation in sparse representation (SR) based recognition algorithm, a fast sparse representation (FSR) algorithm is presented in this paper to improve the efficiency of synthetic aperture radar (SAR) target configuration recognition. Taking the inertia variance characteristic of SAR target images over a small range of azimuth angles into consideration, training samples of each configuration are averaged. Instead of using all the training samples to establish the dictionary in SR, the average samples are utilized to construct the dictionary in FSR. A small dictionary accelerates the speed of the proposed algorithm.

It is very difficult for the millimeter wave (MMW) seeker to detect and capture the target. Tracking the target unstably, even losing the target happens frequently. Focusing on the problem, a simple but effective target recapture method is presented for narrow-beam MMW seeker in this paper. The parameters outputted by the inertial navigation system (INS) and the seeker are utilized to deduce the coordinates of the target. And then, target searching is implemented again on the basis of the deduced coordinates. The target recapture time can be dramatically reduced by using the proposed method, thus guaranteeing enough terminal guidance time. The effectiveness of the proposed method is verified by the mooring test-fly experiments.

Focusing on the problem of the millimeter wave (MMW) seeker performance evaluation, which plays an important role for the terminal control algorithm design, an evaluation method is proposed based on the real-time kinematic (RTK) for moving targets. Firstly, time synchronization is realized for different global position system (GPS) carrier platforms taking a controller as the reference. And then, the key parameters associated with the guidance control are calculated on the basis of the GPS measurements. Finally, parameter comparisons are implemented by using the calculated values and the seeker's outputs. The effectiveness of the proposed MMW seeker evaluation method is verified by the mooring test-fly experiments.

A two-stage sparse structure representation algorithm which can preserve the manifold structure of the data is proposed for synthetic aperture radar target configuration recognition in this paper. Manifold structure of the data is preserved by two stages. In the training stage, taking advantage of both the sparse representation (SR) and manifold learning, local structure of the data is preserved in the reconstruction space, where SR-based recognition is realized. In the testing stage, two structure preserving factors based on the testing samples are embedded into the SR model to enhance structure preserving performance. The first one is constructed to preserve the local structure of the testing samples, which can guarantee the samples that are close to each other in the original space will also be close to each other in the sparse space. And the second one is established to preserve the distant structure of the testing samples, which can ensure the samples that are far from each other in the original space will also be far from each other in the sparse space. Manifold structure of the data is well captured and preserved by two stages. Experimental results on the moving and stationary target acquisition and recognition database demonstrate the effectiveness of the proposed algorithm.

Locality preserving projections (LPP) can preserve the local structure of the datasets effectively. However, it is not capable of separating the samples that are close to each other in the high-dimensional space but belong to different classes. Focusing on the problem, a class-dependent locality preserving projections (CDLPP) algorithm is proposed in this paper. The class information is embedded into the LPP model, and the similarity matrix and the difference matrix are constructed according to the class information. The similarity matrix is utilized to preserve the local structure of the samples belong to the same class, whereas the difference matrix is utilized to separate the samples that are close to each other in the high-dimensional space but belong to different classes. Experiments are conducted using the moving and stationary target acquisition and recognition (MSTAR) database, the results verify the effectiveness of the proposed algorithm.

The performance of the seeker highly influences the design of the control algorithms and the attack precision of the missile. Before the missile with seeker mounted on is launched, the performance of the seeker needs to be accurately evaluated, especially for the expensive ones. Focusing on the problem, a millimeter wave seeker evaluation method is proposed based on the differential global positioning system (DGPS) principle. Firstly, the parameters of the line-of-light (LOS) rates and the missile to target distance are calculated with the data obtained by the DGPS. Then, the results are compared to the ones that are outputted by the seeker itself. The effectiveness of the proposed algorithm is verified on the real seeker data, comparisons with the inertial navigation system (INS) further demonstrate the advantage of the proposed method.

A nonlinear chirp scaling algorithm (NCSA) is proposed for bistatic SAR data processing with high squint angles in tandem configuration. Besides the dependence of the azimuth frequency, the dependence of range is considered for the Doppler chirp rate of tandem bistatic SAR. The proposed algorithm can compensate the range dependence of both the range cell migration (RCM) and the second range compression (SRC) terms well by appropriately setting the coefficients of the phase term, which is obtained after the nonlinear chirp scaling process in the two-dimensional wavenumber domain. Only fast Fourier transforms (FFTs) and phase multiplies are required, and fast imaging is implemented in the frequency domain. Satisfying focusing qualities verify the effectiveness of the proposed algorithm by simulations.

A new way of looking at Loffeld's bistatic formula (LBF) is presented for tandem configuration in this letter. The factors that affect the precision of the spectrum are obtained through the comparison with another analytical one. It has been proved that the cosine of the half bistatic angle plays a more important role respect to the other factor, which is whether the baseline to range ratio is equal to the tangent of the half bistatic angle or not. As long as the cosine of the half bistatic angle is very close to one, the LBF spectrum is of high quality, and it does not have a direct influence by the length of the baseline (baseline-to-range ratio) or the size of the squint angle. The factors that affect the precision of the spectrum are discussed in detail through simulated experiments.