The absence of publicly available, large-scale, high-quality datasets for Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has significantly hindered the application of rapidly advancing deep learning techniques, which hold huge potential to unlock new capabilities in this field. This is primarily because collecting large volumes of diverse target samples from SAR images is prohibitively expensive, largely due to privacy concerns, the characteristics of microwave radar imagery perception, and the need for specialized expertise in data annotation. Throughout the history of SAR ATR research, there have been only a number of small datasets, mainly including targets like ships, airplanes, buildings, etc. There is only one vehicle dataset MSTAR collected in the 1990 s, which has been a valuable source for SAR ATR. To fill this gap, this paper introduces a large-scale, new dataset named ATRNet-STAR with 40 different vehicle categories collected under various realistic imaging conditions and scenes. It marks a substantial advancement in dataset scale and diversity, comprising over 190,000 well-annotated samples-$10\times$ larger than its predecessor, the famous MSTAR. Building such a large dataset is a challenging task, and the data collection scheme will be detailed. Secondly, we illustrate the value of ATRNet-STAR via extensively evaluating the performance of 15 representative methods with 7 different experimental settings on challenging classification and detection benchmarks derived from the dataset. Finally, based on our extensive experiments, we identify valuable insights for SAR ATR and discuss potential future research directions in this field. We hope that the scale, diversity, and benchmark of ATRNet-STAR can significantly facilitate the advancement of SAR ATR.

Target detection in aerial images captured by unmanned aerial vehicles has long been hampered by occlusion. Active Object Detection (AOD) aims to fundamentally address this issue from the active vision perspective, typically realized through the Deep Reinforcement Learning (DRL) paradigm. However, the active observation policy often suffers from low generalization ability, thus limiting its practical application. In this paper, we propose Divide-and-Conquer Sharpness-Aware Gradient Matching (DC-SAGM), a novel sharpness-based Domain Generalization (DG) method, to effectively enhance the generalization capacity of the agent’s policy. Specifically, we train the agent to learn the active observation policy using the conventional DRL approach. Sharpness-Aware Gradient Matching (SAGM) is employed during training, improving the model’s generalization performance by minimizing the sharpness metric of the loss landscape. Nevertheless, the imperfect state representation and classifier preference in the AOD problem lead to fierce gradient conflicts, deteriorating the effectiveness of SAGM. We address this incompatibility by using a divide-and-conquer strategy and exclude gradient conflicts via the majority-rule gradient surgery operation. Extensive experimental results on the UEVAVD dataset validate DC-SAGM’s superiority in helping the agent’s policy achieve better generalization compared to extensive policy learning approaches.

The fundamental challenge in SAR target detection lies in developing discriminative, efficient, and robust representations of target characteristics within intricate non-cooperative environments. However, accurate target detection is impeded by factors including the sparse distribution and discrete features of the targets, as well as complex background interference. In this study, we propose a Gamma Diffusion Model Network with MambaSAR module (MaDiNet) for SAR target detection. Specifically, MaDiNet leverages the Gamma distribution to model the statistical characteristics of SAR images, and conceptulizes SAR target detection as the task of generating target bounding boxes in the image space. Furthermore, we design a MambaSAR module to capture intricate spatial structural information of targets and enhance the capability of the model to differentiate between targets and complex backgrounds. The experimental results on multi-class target detection datasets have all achieved SOTA, with a particularly notable improvement of 6.7% in mAP50 on the ODSOG-1.0 dataset, proving the effectiveness of the proposed network. Code is available at https://github.com/JoyeZLearning/MaDiNet.

Despite the remarkable progress in synthetic aperture radar automatic target recognition (SAR ATR), recent efforts have concentrated on detecting and classifying a specific category, e.g., vehicles, ships, airplanes, or buildings. One of the fundamental limitations of the top-performing SAR ATR methods is that the learning paradigm is supervised, task-specific, limited-category, closed-world learning, which depends on massive amounts of accurately annotated samples that are expensively labeled by expert SAR analysts and have limited generalization capability and scalability. In this work, we make the first attempt towards building a foundation model for SAR ATR, termed SARATR-X. SARATR-X learns generalizable representations via self-supervised learning (SSL) and provides a cornerstone for label-efficient model adaptation to generic SAR target detection and classification tasks. Specifically, SARATR-X is trained on 0.18 M unlabelled SAR target samples, which are curated by combining contemporary benchmarks and constitute the largest publicly available dataset till now. Considering the characteristics of SAR images, a backbone tailored for SAR ATR is carefully designed, and a two-step SSL method endowed with multi-scale gradient features was applied to ensure the feature diversity and model scalability of SARATR-X. The capabilities of SARATR-X are evaluated on classification under few-shot and robustness settings and detection across various categories and scenes, and impressive performance is achieved, often competitive with or even superior to prior fully supervised, semi-supervised, or self-supervised algorithms. Our SARATR-X and the curated dataset are released at https://github.com/waterdisappear/SARATR-X to foster research into foundation models for SAR image interpretation.

Sparse aperture ISAR imaging for maneuvering targets is a relatively difficult task due to the complex form of observation model. In this letter, an approximated observation model based on CZT and NCS is proposed to accelerate the implementation of forward and backward operators. A structured sparse prior is introduced to establish a statistical framework for SA-ISAR imaging and VB-GAMP is utilized to implement a fast inference. A rotation parameters estimation based on image quality optimization is further plugged in the reconstruction procedure to achieve joint imaging and motion compensation. Experiments on simulated and measured data validate the effectiveness and efficiency of the proposed method.

The existence of adversarial examples causes serious security risks when deep neural networks are applied to synthetic aperture radar (SAR) target detection. In SAR image processing, the added small disturbances can cause the model to output incorrect predictions. Due to the multipath effect in the propagation of detection signals, there are complex interactions between targets and their surroundings serving as supportive clues for target detection. The interactions are manifested as tight correlations between pixels and contextual information in the SAR image (where context refers to various relationships, e.g., target-to-target co-occurrence relationships). In this letter, we proposed a novel conditional random field-based adversarial attack (CRFA) method, which disturbs the intrinsic interactions between the target and its surroundings. To the best of our knowledge, we are the first to exploit the contextual information for attacking the SAR target detector. We formulate the attack as an optimization problem and design the context information loss to calculate the energy differences in local feature patterns before and after perturbation. By maximizing the energy differences, the context area information around the target is destroyed, and the detector outputs the candidate box with a slight shift, even ignoring the ground truth and missing targets. Extensive experimental results on the SAR Ship Detection dataset (SSDD) demonstrate that our proposed algorithm reduces mAP by 4.29% on existing object detection models, validating the effectiveness of the method.

Aircraft target detection in SAR images is a challenging task due to the discrete scattering points and severe background clutter interference. Currently, methods with convolution-based or transformer-based paradigms cannot adequately address these issues. In this letter, we explore diffusion models for SAR image aircraft target detection for the first time and propose a novel Diffusion-based aircraft target Detection network for SAR images (DiffDet4SAR). Specifically, the proposed DiffDet4SAR yields two main advantages for SAR aircraft target detection: 1) DiffDet4SAR maps the SAR aircraft target detection task to a denoising diffusion process of bounding boxes without heuristic anchor size selection, effectively enabling large variations in aircraft sizes to be accommodated; and 2) the dedicatedly designed Scattering Feature Enhancement (SFE) module further reduces the clutter intensity and enhances the target saliency during inference. Extensive experimental results on the SAR-AIRcraft-1.0 dataset show that the proposed DiffDet4SAR achieves 88.4% mAP50, outperforming the state-of-the-art methods by 6%. Code is availabel at https://github.com/JoyeZLearning/DiffDet4SAR.

Learning based approaches have witnessed great successes in blind single image super-resolution (SISR) tasks, however, handcrafted kernel priors and learning based kernel priors are typically required. In this paper, we propose a meta-learning and Markov Chain Monte Carlo (MCMC) based SISR approach to learn kernel priors from organized randomness. In concrete, a lightweight network is adopted as kernel generator, and is optimized via learning from the MCMC simulation on random Gaussian distributions. This procedure provides an approximation for the rational blur kernel, and introduces a network-level Langevin dynamics into SISR optimization processes, which contributes to preventing bad local optimal solutions for kernel estimation. Meanwhile, a meta-learning based alternating optimization procedure is proposed to optimize the kernel generator and image restorer, respectively. In contrast to the conventional alternating minimization strategy, a meta-learning based framework is applied to learn an adaptive optimization strategy, which is less-greedy and results in better convergence performance. These two procedures are iteratively processed in a plug-and-play fashion, for the first time, realizing a learning-based but plug-and-play blind SISR solution in unsupervised inference. Extensive simulations demonstrate the superior performance and generalization ability of the proposed approach when compared with the Start-of-the-Art solutions on synthesis and real-world datasets.

For inverse synthetic aperture radar (ISAR) imaging under sparse aperture (SA) conditions, the rotation motion compensation is seldom considered. However, with the improvement of resolution, the migration through resolution cell (MTRC) cannot be ignored. Traditional methods for rotation motion compensation generally fail in SA cases. This article proposes a method to jointly implement sparse imaging and compensation of the MTRC in a structured sparse Bayesian learning (SBL) framework. Due to the coupling of fast time and slow time, the observation model is established in a vectorized form. To reduce the computational complexity, approximated inference methods are utilized to achieve fast inference for the posteriors. Maximum contrast (MC) criterion is adopted to estimate the rotation parameters. The approximated implementation for the forward operator and backward operator is discussed to further accelerate the algorithm. Experimental results based on simulated and measured data validate the effectiveness and efficiency of the proposed methods.

A multistatic ISAR system can observe a target from multiple observation angles. Compared with the monostatic ISAR system, the multistatic ISAR system can obtain more spatial sampling data, which provides the ability for high-resolution ISAR imaging. In some cases, the locations of radars are close. There are overlaps among observing angles, which brings little cross-range resolution improvement. However, such scenes are less considered in previous work. In the scene with overlaps, the image obtained by each radar may be similar due to the similar observation angles. In this letter, a novel multistatic ISAR imaging model is proposed by applying the similarity prior as a constraint. And an effecient image reconstruction algorithm is derived based on the orthogonality of observation matrix. Compared with existing CS based methods, the proposed method can be directly applied on multistatic ISAR echoes without pre-processing of rearranging, which is more convenient in practical applications. Experiment results of simulated and measured data show that the proposed method achieves better performance especially under low signal-to-noise (SNR) conditions.

With the maturity of deep learning algorithm in Synthetic Aperture Radar (SAR) target recognition filed, Convolutional Neural Network (CNN) has become the most effective model. However, the interpretability and the separability of feature maps extracted from convolution layers have not been specially analyzed neither qualitatively nor quantitatively, which makes the traditional model work like a “black box”. To alleviate the problem, a novel model based on separability measure (SM) - CNN is proposed in this letter, which introduces the principle of maximal coding rate reduction to the backbone module. SM-CNN quantitatively analyzes the separability of the feature maps and takes the value as a vital part of the loss function to guide the training process of the model. The calculation process of the separability measure values can be strictly derived mathematically, so it is more interpretable, turning the black box into a “gray box”. Additionally, the proposed model can achieve comparable recognition performance of the backbone networks with reduced computational complexity. Comparative experiments based on MSTAR and OpenSARShip data sets verify the effectiveness and practicability of the method proposed in this letter.

Compared to 2D inverse synthetic aperture radar (ISAR) images of a space target, its 3D model can provide adequate details and accurate measurement parameters. However, it is challenging to tackle the problem of feature extraction and correlation during 3D reconstruction of space targets purely based on radar image sequences, due to their lack of clear evidence in imaging similarity compared to optical images. To address this problem, this paper proposes radar neural radiance fields (i.e. RaNeRF), which is a novel 3D reconstruction method using only observed ISAR image sequences. Firstly, the 3D structure of a target is represented as a continuous 6D function of space positions and viewing directions using a fully-connected deep network. Secondly, the relationship between the 3D structure and 2D ISAR images of the target is constructed to enable differential rendering of ISAR images. Our overall pipeline can thus be trained using the discrepancy between the modulus of rendered and observed ISAR images in a purely self-supervised manner without 3D supervision. Finally, the 3D mesh model of the target can be retrieved from the learned density field via marching cube. As a result, the proposed RaNeRF can directly reconstruct the 3D structure of targets without explicit feature extraction and correlation of ISAR image sequences. Both quantitative and qualitative results verify the effectiveness of the proposed method. Compared to conventional baseline methods using point clouds, our reconstructed structure is more complete and accurate. In addition, the optimized model can synthesize ISAR images at novel observation direction, which can be used for downstream tasks including data augmentation and target recognition.

This paper addresses the problem of detecting radar targets submerged into strong sea clutter background. In this study, a novel type of detection method based on matrix information geometry (MIG) is developed. Filtering process and manifold projection are incorporated into detector design. Firstly, a filtering scheme for correlation coefficients is established via subband decomposition, such that a subband Hermitian positive definite (HPD) manifold constructed by a set of subband HPD matrices is formulated. Accordingly, the detection is performed as discriminating the target and the clutter on the subband HPD manifold. Then, in order to enhance the discriminative power between the target and the strong clutter, a manifold projection method that maps the HPD manifold into a lower-dimensional and more discriminative one is devised. In this study, the manifold projection is formulated as an optimization problem on a Stiefel manifold according to the principle of maximizing signal-to-clutter ratio (SCR). Subsequently, a manifold projection based subband MIG detector is proposed. Extensive experiments based on simulated data and real radar data are carried out to verify the effectiveness of the proposed method. The experimental results demonstrate that the proposed method can efficiently suppress the strong sea clutter and achieve better detection performance than the competitors.

Recently, deep unfolding networks with interpretable parameters have been widely utilized in direction of arrival (DOA) estimation due to the faster convergence speed and better generalization ability. However, few consider the nested array for gridless DOA estimation. In this letter, we propose a deep alternating projection network to address the problem. We first convert the covariance matrix into a measurement vector in the form of atomic norm, which can reduce the matrix dimension during projection. We then train the proposed network to alternately obtain the positive semi-definite matrix and the corresponding irregular Hermitian Toeplitz matrix, where the loss function is derived by employing the trace of network output. Finally, we apply the irregular root Multiple Signal Classification (MUSIC) method to obtain gridless DOA via nested array. We demonstrate that the proposed networks can accelerate the convergence rate and reduce computational cost. Simulations verify the performance of proposed networks in comparison with the existing methods.

In an era of ubiquitous large-scale evolving data streams, data stream clustering (DSC) has received lots of attention because the scale of the data streams far exceeds the ability of expert human analysts. It has been observed that high-dimensional data are usually distributed in a union of low-dimensional subspaces. In this article, we propose a novel sparse representation-based DSC algorithm, called evolutionary dynamic sparse subspace clustering (EDSSC). It can cope with the time-varying nature of subspaces underlying the evolving data streams, such as subspace emergence, disappearance, and recurrence. The proposed EDSSC consists of two phases: 1) static learning and 2) online clustering. During the first phase, a data structure for storing the statistic summary of data streams, called EDSSC summary, is proposed which can better address the dilemma between the two conflicting goals: 1) saving more points for accuracy of subspace clustering (SC) and 2) discarding more points for the efficiency of DSC. By further proposing an algorithm to estimate the subspace number, the proposed EDSSC does not need to know the number of subspaces. In the second phase, a more suitable index, called the average sparsity concentration index (ASCI), is proposed, which dramatically promotes the clustering accuracy compared to the conventionally utilized SCI index. In addition, the subspace evolution detection model based on the Page-Hinkley test is proposed where the appearing, disappearing, and recurring subspaces can be detected and adapted. Extinct experiments on real-world data streams show that the EDSSC outperforms the state-of-the-art online SC approaches.

Sparse aperture inverse synthesis aperture radar (SA-ISAR) imaging is generally solved by compressed sensing (CS) methods or sparse signal recovery (SSR). Many SSR methods focus on the sparsity of radar images only, which achieves unsatisfactory results on structural data. In addition, most of the traditional CS algorithms suffer from a heavy computational burden. In this article, a new deep unfolding network called pattern-coupled sparse Bayesian learning (PCSBL)-generalized approximate message passing (GAMP)-Net is proposed. The proposed network structure can learn the model of block-sparse information from data to reconstruct images of better quality via fewer iteration steps. First, a complex-valued pattern-coupled hierarchical Gaussian prior model is established. Then, the GAMP algorithm is applied for computational Bayesian inference. Based on the previous PCSBL-GAMP framework, the iterative procedure is unrolled to be a deep network structure. A complex-valued backpropagation (BP) algorithm is derived for network training. Experiment results based on simulated and measured data validate the superiority of the proposed method over the traditional PCSBL-GAMP algorithm. Also, the proposed algorithm is ten times faster than the traditional PCSBL-GAMP algorithm.

This study proposes a model-driven deep network based on the linear alternating direction method of multipliers (L-ADMM), to solve the problem whereby the inverse synthetic aperture radar (ISAR) generates defocused images of targets exhibiting micro-motion with sparse aperture. The network unfolds the operation process of L-ADMM into a model-driven deep network, and automatically optimizes the parameters of the network through learning instead of manually adjusting the parameters, which can better obtain images. Analyses of data acquired through simulations and experimental measurements were used to compare the results of imaging obtained by L-ADMM-net with those of the range Doppler (R-D) algorithm, chirplet algorithm, and L-ADMM. The entropy of images obtained by L-ADMM-net was the lowest, and their image contrast and resolution were the highest. Moreover, L-ADMM-net can generate high-resolution images of targets exhibiting micro-motion with sparse aperture at a low signal-to-noise ratio (SNR), which verifies its robustness. It can also automatically update and adjust parameters more stably than L-ADMM. The proposed method significantly improves the resolution, robustness, and stability of images of targets exhibiting micro-motion in different situations compared with traditional methods, and can provide technical support for target recognition in the future.

Recently, the matrix information geometry (MIG) detector, which characterizes sample data as a Hermitian positive definite (HPD) matrix located on the HPD manifold, was rapidly developed and demonstrated extraordinary performance in numerous applications, especially in heterogeneous clutter backgrounds. In this paper, the geodesic normal coordinate (GNC)-based manifold filter is proposed to improve the detection performance of the MIG detector in strong clutter backgrounds. Using the GNC system, the distribution of target echoes and clutter on the high-dimensional manifold can be visualized and analyzed. Moreover, by exploiting the information concerning the distribution of matrices, the manifold filter is proposed to enhance target echoes and suppress strong clutter. Then, the manifold-filter-based MIG detector is designed, and its superiority is theoretically analyzed. The actual clutter data is utilized to verify the effectiveness of the proposed method. The results show that the proposed manifold filter achieves a signal-to-clutter ratio improvement of more than 5 dB over the existing MIG detectors.

Inverse synthetic aperture radar (ISAR) imaging for the target with micro-motion parts is influenced by the micro-Doppler (m-D) effects. In this case, the radar echo is generally decomposed into the components from the main body and micro-motion parts of target, respectively, to remove the m-D effects and derive a focused ISAR image of the main body. For the sparse aperture data, however, the radar echo is intentionally or occasionally under-sampled, which defocuses the ISAR image by introducing considerable interference, and deteriorates the performance of signal decomposition for the removal of m-D effects. To address this issue, this paper proposes a novel m-D effects removed sparse aperture ISAR (SA-ISAR) imaging algorithm. Note that during a short interval of ISAR imaging, the range profiles of the main body of target from different pulses are similar, resulting in a low-rank matrix of range profile sequence of main body. For the range profiles of the micro-motion parts, they either spread in different range cells or glint in a single range cell, which results in a sparse matrix of range profile sequence. From this perspective, the low-rank and sparse properties are utilized to decompose the range profiles of the main body and micro-motion parts, respectively. Moreover, the sparsity of ISAR image is also utilized as a constraint to eliminate the interference caused by sparse aperture. Hence, SA-ISAR imaging with the removal of m-D effects is modeled as a triply constrained underdetermined optimization problem. The alternating direction method of multipliers (ADMM) and linearized ADMM (L-ADMM) are further utilized to solve the problem with high efficiency. Experimental results based on both simulated and measured data validate the effectiveness of the proposed algorithm.

Inverse synthetic aperture radar (ISAR) imaging for the sparse aperture data is affected by considerable artifacts, because under-sampling of data produces high-level grating and side lobes. Noting the ISAR image generally exhibits strong sparsity, it is often obtained by sparse signal recovery (SSR) in case of sparse aperture. The image obtained by SSR, however, is often dominated by strong isolated scatterers, resulting in difficulty to recognize the structure of target. This paper proposes a novel approach to enhance the ISAR image obtained from the sparse aperture data. Although the scatterers of target are isolated in the ISAR image, they should be associated with the neighborhood to reflect some intrinsic structural information of the target. A convolutional reweighted l1minimization model, therefore, is proposed to model the structural sparsity of ISAR image. Specifically, the ISAR image is reconstructed by solving a sequence of reweighted l1problems, where the weight of each pixel used for the next iteration is calculated from the convolution of its neighbor values in the current solution. The problem is solved by the alternating direction of multipliers (ADMM) and linearized approximation, respectively, to improve the computational efficiency. Experimental results based on both simulated and measured data validate that the proposed algorithm is effective to enhance the ISAR image, robust to noise, and more impressively, very efficient to implement.

The micro-Doppler (m-D) effect caused by micro-motion degrades the readability of the inverse synthetic aperture radar (ISAR) image. To achieve well-focused ISAR image of the target with the micro-motion part, this paper proposes a novel approach for the removal of m-D effect of ISAR image. Note that the range profiles of the rigid body are similar to each other, making the respective data matrix low-rank. Those of the micro-motion part, in contrary, generally fluctuate in different range cells, whose data matrix is sparse. Therefore, the removal of m-D effect can be naturally solved by the robust principal component analysis (RPCA)-a convenient convex program to decompose an auxiliary matrix into a low-rank matrix and a sparse one. In RPCA, the rank of a matrix is described by the nuclear norm, which is convex but leads to a suboptimal solution. To address it, we utilize a nonconvex surrogate, i.e., the summation of logistic function of the singular values of a matrix, to approximate the rank. Moreover, the range profiles of the rigid body are generally locally similar. To capture this geometric structured information, we further introduce a Laplacian regularization into the model. Then, the Laplacian regularized nonconvex low-rank (LRNL) model is solved efficiently by the linearized alternating direction method (ADM). Extensive experimental results based on both simulated and measured data demonstrate the effectiveness of the proposed approach on the removal of m-D effect of ISAR image.

We present the convolution neural networks (CNNs) to achieve the localization of near‐field sources via the symmetric double‐nested array (SDNA). Considering that the incoherent near‐field sources can be separated in the frequency spectrum, we first calculate the phase difference matrices and consider the typical elements as the inputs of the networks. In order to guarantee the precision of the angle‐of‐arrival (AOA) estimation, we implement the autoencoders to divide the AOA subregions and construct the corresponding classification CNNs to obtain the AOAs of near‐field sources. Then, we construct a particular range vector without the estimated AOAs and utilize the regression CNN to obtain the range parameters of near‐field sources. The proposed algorithm is robust to the off‐grid parameters and suitable for the scenarios with the different number of near‐field sources. Moreover, the proposed method outperforms the existing method for near‐field source localization.

As one of the most important features for human perception, contours are widely applied in graphics and mapping applications. However, it is considerably challenging to extract contours from large-scale point clouds due to the irregular distribution of point clouds. In this letter, we propose a 3-D-guided multiconditional residual generative adversarial network (3-D-GMRGAN), the first deep-learning framework to generate contours for large-scale outdoor point clouds. To make the network handle huge amounts of points, we operate contours in the parametric space rather than raw point space, associated with a parametric chamfer distance. Then, to gather contour features from potential positions and avoid the huge solution space, we propose a guided residual generative adversarial framework, by utilizing a simple feature-based method to get the “over extraction” potential contour distribution. Experiments demonstrate that the proposed method is able to generate contours efficiently for large-scale point clouds, with fewer outliers and pseudo contours compared with state-of-the-art approaches.

In the case of sparse aperture, the coherence between pulses of radar echo is destroyed, which challenges inverse synthetic aperture radar (ISAR) autofocusing and imaging. Mathematically, reconstructing the ISAR image from the sparse aperture radar echo is a linear underdetermined inverse problem, which, by nature, can be solved by the fast developed compressive sensing (CS) or sparse signal recovery theory. However, the CS-based sparse aperture ISAR imaging algorithms are generally computationally heavy, which becomes the bottleneck of preventing their applications to the real-time ISAR imaging system. In this article, we propose a novel and computationally efficient ISAR autofocusing and imaging algorithm for sparse aperture. We first consider a generalized CS model for ISAR imaging and autofocusing with sparse and entropy-minimization regularizations, and then utilize the alternating direction method of multipliers (ADMM) algorithm to optimize the model. To improve computational efficiency, the matrix inversion is translated to an elementwise division with the usage of a partial Fourier dictionary, and the 2-D ISAR image is updated as a whole instead of range cellwise. To achieve autofocusing for sparse aperture, the phase error is estimated by minimizing the entropy of the ISAR image reconstructed in each iterative loop. Experiments based on both simulated and measured data validate that the proposed algorithm can achieve well-focused ISAR images within a few seconds, which is ten times faster than the reported sparse aperture ISAR imaging algorithms.

For sparse aperture (SA) radar imaging, the phase errors are difficult to be estimated, which challenges the traditional autofocusing for inverse synthetic aperture radar (ISAR) imaging. A novel Bayesian ISAR autofocusing algorithm for SA is proposed. We unfold the sparse Laplace prior to two layers so that the full variational Bayesian inference can be derived. To further exploit the prior knowledge on the structure of radar images, dependencies among adjacent pixels are considered to design a structured sparse prior. In addition, the minimum entropy criterion is utilized to estimate the phase error during the reconstruction of the ISAR image to achieve ISAR autofocusing. The superiority of the proposed method against the traditional sparsity-driven method is validated by the experimental results based on both simulated and measured data.

Sparse aperture ISAR autofocusing and imaging is generally achieved by methods of compressive sensing (CS), or, sparse signal recovery, because non-uniform sampling of sparse aperture disables fast Fourier transform (FFT)-the core of traditional ISAR imaging algorithms. Note that the CS based ISAR autofocusing methods are often computationally heavy to execute, which limits their applications in real-time ISAR systems. The improvement of computational efficiency of sparse aperture ISAR autofocusing is either necessary or at least highly desirable to promote their practical usage. This paper proposes an efficient sparse aperture ISAR autofocusing algorithm. To eliminate the effect of sparse aperture, the ISAR image is reconstructed by sparse Bayesian learning (SBL), and the phase error is estimated by minimum entropy during the reconstruction of ISAR image. However, the computation of expectation in SBL involves a matrix inversion with an intolerable computational complexity of at least O(L3). Here, in the Bayesian inference of SBL, we transform the time-consuming matrix inversion into an element-wise matrix division by the alternating direction method of multipliers (ADMM). An auxiliary variable is introduced to divide the computation of posterior into three simpler subproblems, bringing computational efficiency improvement. Experimental results based on both simulated and measured data validate the effectiveness as well as high efficiency of the proposed algorithm. It is 20-30 times faster than the SBL based sparse aperture ISAR autofocusing approach.

Obtained by wide band radar system, high resolution range profile (HRRP) is the projection of scatterers of target to the radar line-of-sight (LOS). HRRP reconstruction is unavoidable for inverse synthetic aperture radar (ISAR) imaging, and of particular usage for target recognition, especially in cases that the ISAR image of target is not able to be achieved. For the high-speed moving target, however, its HRRP is stretched by the high order phase error. To obtain well-focused HRRP, the phase error induced by target velocity should be compensated, utilizing either measured or estimated target velocity. Noting in case of under-sampled data, the traditional velocity estimation and HRRP reconstruction algorithms become invalid, a novel HRRP reconstruction of high-speed target for under-sampled data is proposed. The Laplacian scale mixture (LSM) is used as the sparse prior of HRRP, and the variational Bayesian inference is utilized to derive its posterior, so as to reconstruct it with high resolution from the under-sampled data. Additionally, during the reconstruction of HRRP, the target velocity is estimated via joint constraint of entropy minimization and sparseness of HRRP to compensate the high order phase error brought by the target velocity to concentrate HRRP. Experimental results based on both simulated and measured data validate the effectiveness of the proposed Bayesian HRRP reconstruction algorithm.

The data streams arising in many applications can be modeled as a union of low-dimensional subspaces known as multi-subspace data streams (MSDSs). Clustering MSDSs according to their underlying low-dimensional subspaces is a challenging problem which has not been resolved satisfactorily by existing data stream clustering (DSC) algorithms. In this paper, we propose a sparse-based DSC algorithm, which we refer to as dynamic sparse subspace clustering (D-SSC). This algorithm recovers the low-dimensional subspaces (structures) of high-dimensional data streams and finds an explicit assignment of points to subspaces in an online manner. Moreover, as an online algorithm, D-SSC is able to cope with the time-varying structure of MSDSs. The effectiveness of D-SSC is evaluated using numerical experiments.

Convolutional neural networks (CNN) have successfully been employed to tackle several remote sensing tasks such as image classification and show better performance than previous techniques. For the radar imaging community, a natural question is: Can CNN be introduced to radar imaging and enhance its performance? This letter gives an affirmative answer to this question. We first propose a processing framework by which a complex-valued CNN (CV-CNN) is used to enhance radar imaging. Then we introduce two modifications to the CV-CNN to adapt it to radar imaging tasks. Subsequently, the method to generate training data is shown and some implementation details are presented. Finally, simulations and experiments are carried out, and both results show the superiority of the proposed method on imaging quality and computational efficiency.

For sparse aperture (SA) radar echoes, the coherence between the undersampled pulses is destroyed, which challenges the effectiveness of the traditional autofocusing and scaling in inverse synthetic aperture radar (ISAR) imaging. A novel Bayesian ISAR autofocusing and scaling algorithm for sparse aperture is proposed, which utilizes Laplacian scale mixture, as the sparse prior of ISAR image, and variational Bayesian inference based on the Laplacian approximation to derive its posterior. In addition, it learns the phase error, rotational velocity, and center of target from radar echo automatically during the reconstruction of ISAR image, so as to achieve ISAR autofocusing and scaling for SA. Because the parameters learning is not easy to converge with the undersampled data, a modified Newton method based on joint constraint of entropy and sparsity is proposed to guarantee fast convergence in a right direction. Experimental results based on both simulated and measured data validate the robustness of the proposed ISAR imaging algorithm against SA and noise.

Recently, millimeter-wave (MMW) 3D holography techniques employing a scanning 1D multiple input multiple output (MIMO) array have shown several superiorities for short-range applications than traditional single input single output (SISO) ones. However, current imaging algorithms for this emerging regime are not satisfied, either too slow as a back projection (BP) manner is used or of poor quality since several steps of approximations are introduced. In this paper, two fast fully-focused imaging algorithms are developed towards fixing these drawbacks. Both algorithms are based on the assumption that the receivers are evenly distributed. The first algorithm further hypothesizes that the transmitters are also evenly located, while the second algorithm needs looser restrictions that the transmitters can be arbitrarily positioned. The frequency domain expressions of the modified Kirchhoff method are also derived and used to promote the precision of the proposed algorithms. In addition, several implementation issues including resolution, sampling criteria and computational complexity are discussed. Finally, both simulation and experimental results validate the effectiveness of the proposed methods on reconstruction quality and computational efficiency.

This paper proposes a novel bistatic inverse synthetic aperture radar (ISAR) imaging algorithm for the target with complex motion under low signal to noise ratio (SNR) condition. Note the bistatic ISAR system generally suffers from a lower SNR than the monostatic one because of its non-mirror reflection geometry. A de-noising method, therefore, is proposed to improve SNR of range profiles, which accumulates the aligned range profiles non-coherently to obtain a window for noise suppression. Additionally, since the complex motion of target induces nonstationary Doppler, which is destructive to ISAR imaging, an optimal coherent processing interval (CPI) selection algorithm is further proposed to find out the interval where the Doppler is relatively stationary, so as to produce well-focused ISAR images. It utilizes the reassigned time-frequency (TF) method to obtain the high resolution instantaneous Doppler spectrum, and the minimum entropy criterion to select the optimal CPI, respectively. Note the selected CPI often contains too limited pulses to produce ISAR images with high resolution. A sparse aperture ISAR imaging method within the Bayesian framework is further proposed, which introduces the Laplacian scale mixture (LSM) model as the sparse prior, so as to reconstruct well-focused ISAR images with high resolution and low side lobes from the limited data. Compared with the traditional sparse Bayesian learning method, the proposed LSM based ISAR imaging performs superiorly on resolution improvement and noise reduction. Experimental results based on both simulated and measured data validate the effectiveness of the proposed algorithms.

High resolution range profile (HRRP) recognition is a widely used technique in the radar automatic target recognition community. However, the recognition performance often suffers from aspect sensitivity and the unsuitable features designed in practical applications. Aiming at dealing with these problems, we propose a new type of stochastic neural network model named Infinite Classification Restricted Boltzmann Machine (RBM) in this paper, which originates from the Infinite RBM and the Classification RBM. Different from most conventional methods using separate models for each aspect frame, the proposed model can jointly model HRRPs from different aspects with a unified stochastic model, which benefits from its power of learning complex distribution of data. Besides that, our model can adaptively learn suitable features according to specific recognition tasks. Owing to these two properties, the proposed model learns discriminative features more efficiently with relative smaller training data than other traditional recognition techniques. Experiment results using simulated HRRP data indicate that our model has more generalization power and better recognition performance than the Classification RBM.

Quantum radar cross section (QRCS) is studied in this paper, which has an objective measure of the quantum scattering ability of a specified target. The interaction process between quantum radar and the target is introduced, and new results about quantum scattering characteristics of typical targets are reported. Simulation results demonstrate that the number of side lobes of QRCS is dependent on the target size and the interatomic distance; values of QRCS increase with the increasing of the signal photon frequency. For the cylinder target, QRCS increased as the radius increases. The work and results can be beneficial to the design of quantum radar system as well as the development of remote sensing of earth observation.

The compressive sensing(CS) and sparse representation(SR) technique provide new way to improve the ISAR image resolution. At present, in most traditional CS and SR based ISAR imaging methods, the target is regarded as a set of isolated scattering centers. As a matter of fact, adjacent scattering centers compose a lot of small sets which can reflect the structure of the target. It is no doubt that these small sets can be exploited to improve the ISAR image quality, but the traditional CS and SR ISAR imaging methods ignore this. So in this paper, a block sparse signal recovery algorithm-Block Sparse Bayesian Learning(BSBL) method is introduced to modify the traditional method, and the simulate experiment show that this method can get better ISAR image than the other methods.

In this paper, novel knowledge‐aided space‐time adaptive processing (KA‐STAP) algorithms using sparse representation/recovery (SR) techniques by exploiting the spatio‐temporal sparsity are proposed to suppress the clutter for airborne pulsed Doppler radar. The proposed algorithms are not simple combinations of KA and SR techniques. Unlike the existing sparsity‐based STAP algorithms, they reduce the dimension of the sparse signal by using prior knowledge resulting in a lower computational complexity. Different from the KA parametric covariance estimation (KAPE) scheme, they estimate the covariance matrix using SR techniques that avoids complex selections of the Doppler shift and the covariance matrix taper. The details of the selection of potential clutter array manifold vectors according to prior knowledge are discussed and compared with the KAPE scheme. Moreover, the implementation issues and the computational complexity analysis for the proposed algorithms are also considered. Simulation results show that our proposed algorithms obtain a better performance and a lower complexity compared with the sparsity‐based STAP algorithms and outperform the KAPE scheme in presence of errors in prior knowledge.

In this paper, we have proposed a phase-derived-range (PDR) method for direct sampling of linear-frequency-modulated radar signals. This method is capable of measuring multiple scatterers' ranges of a target and yields root-mean-squared range error values at subwavelength level; therefore, it has great potential to measure micromotions of moving targets and is of significant importance for target recognition. The main challenge that we have solved is extracting ambiguous Doppler phases from high-resolution range profiles generated through a match filter. For guiding the implementation in radar systems, restraint conditions of radar parameters have been systematically justified based on the principle of ambiguity resolution. We have provided a systematic algorithm flowchart for PDR, especially for wideband direct sampling radars. Both simulated and experimental results under different radar parameter settings are presented and validate the performance.

Coherent compensation is a significant problem to multiband radar signal fusion. At present, the dominant root-MUSIC and linear least squares(LLS) based ways use band extrapolation (BWE) to generate the whole band signals, and then apply optimization to get the incoherent phases(ICPs). The precision of these methods are limited by the BWE error and the non-corresponding poles. Furthermore, the high dimensional optimization imposes a heavy burden on the computer. In order to improve the performance of the coherent compensation, the incoherent factors between sub-band signals are analyzed first, then in order to correct the non-corresponding poles, a poles correction process which makes use of minimum entropy principle is presented. Based on these, this paper provides an efficient way to estimate the ICPs of sub-band radar signals. Applications to simulated data verify that the proposed method can get better ICPs estimation results.

Target detection, tracking and classification are three essential and closely coupled subjects for most surveillance systems. In the finite set statistics (FISST) framework, this paper presents a Bayesian and recursive solution to joint detection, tracking and classification (JDTC) of a manoeuvring target in a cluttered environment, which is inspired by previous work on joint target tracking and classification in the classical Bayesian filter framework. The derived JDTC algorithm exploits the dependence of target state on target class by using class‐dependent dynamical model sets. The relative merits of this JDTC algorithm are demonstrated via a two‐dimensional example using a sequential Monte Carlo implementation. It is shown that handling those three closely coupled subjects jointly can achieve comparable detection and tracking performance to that of the exact filter in the FISST framework with a prior known class. The classification results are consistent with the previous work.

Time–frequency distribution (TFD) has been widely used for micro‐Doppler analysis in radar signal processing. However, the spectrogram will suffer from aliasing if the maximum Doppler frequency exceeds half of the pulse repetition frequency, which may lead to false estimation of the targets' kinematic properties. In this study, by transmitting a series of random pulse repetition interval (RPRI) pulses, a concise TFD approach named short‐time compressed sensing (STCS) is proposed for aliasing‐free micro‐Doppler analysis. In STCS, precise analysis and synthesis of the random sampling time series can be achieved by exploiting the signal's sparsity in the frequency domain. Furthermore, adaptive to the data, the widths of the particular rectangle windows are determined by sequential processing with a proper optimisation rule. To speed up the STCS procedure, the smoothed L0 algorithm is chosen for sparse recovery, where the pseudoinverse of the dictionaries can be calculated iteratively. The simulation results indicate that the proposed STCS approach can achieve both preferable TFD and acceptable computational cost. The effectiveness of the STCS is finally verified by the application for micro‐Doppler estimating in RPRI radar.

In this study, a novel sparsity‐based space–time adaptive processing algorithm based on the complex‐valued Homotopy technique is proposed for airborne radar applications. The proposed algorithm firstly extends the existing standard real‐valued Homotopy method to a more general complex‐valued application using the gradient approaches. By exploiting the sparsity of the clutter spectrum in the whole spatiotemporal plane, the proposed algorithm recovers the clutter spectrum via the proposed complex Homotopy algorithm and then uses it to estimate the clutter covariance matrix, followed by the space–time filtering and the target detection. Furthermore, the implementations of the proposed algorithm are detailed. The computational complexity analysis shows that the proposed algorithm has a lower‐computational complexity than the existing complex‐valued Homotopy algorithm. Simulation results show that the proposed algorithm converges at a very fast speed (only 4–6 snapshots in the authors simulations) and provides both excellent detection performance and easy parameter settings.

In this letter, a novel method for inverse synthetic aperture radar (ISAR) imaging under a low signal-to-noise ratio (SNR) condition is presented. The method is a preprocess of the range profiles before motion compensation and is based on the pseudomatched filter, whose impulse response is obtained by conjugating and reversing the average of the coarsely aligned range profile envelopes. With the utilization of the presented method, the SNR of the target range profiles is improved, the conventional ISAR motion compensation methods perform much better, and the ISAR image result is much better focused under a low SNR condition. Experimental results based on both the simulated and real data of an aircraft validate the performance of the presented method.

Motivated by classical coincidence imaging which has been realized in optical systems, an instantaneous microwave-radar imaging technique is proposed to obtain focused high-resolution images of targets without motion limitation. Such a radar coincidence imaging method resolves target scatterers based on measuring the independent waveforms of their echoes, which is quite different from conventional radar imaging techniques where target images are derived depending on time-delay and Doppler analysis. Due to the peculiar features of coincidence imaging, there are two potential advantages of the proposed imaging method over the conventional ones: 1) shortening the imaging time to even a pulse width without resolution deterioration so as to improve the performance of processing noncooperative targets and 2) simplifying the receiver complexity, resulting in a lower cost and platform flexibility in application. The basic principle of radar coincidence imaging is to employ the time-space independent detecting signals, which are produced by a multitransmitter configuration, to make scatterers located at different positions reflect independent waveforms from each other, and then to derive the target image based on the prior knowledge of this detecting signal spatial distribution. By constructing the mathematic model, the necessary conditions of the transmitting waveforms are analyzed for achieving radar coincidence imaging. A parameterized image-reconstruction algorithm is introduced to obtain high resolution for microwave radar systems. The effectiveness of this proposed imaging method is demonstrated via a set of simulations. Furthermore, the impacts of modeling error, noise, and waveform independence on the imaging performance are discussed in the experiments.

A novel method of precise radial range measurement based on wideband radar phase is presented in this paper for micromotion characteristic acquisition. The advantage of this method is the high precision with root-mean-square error values at subwavelength levels, while its difficulties are phase extraction from wideband radar echoes and resolution of ambiguous phase. After the analysis of the principle of radial range measurement based on radar phase, the method of extracting the Doppler phase from wideband radar echoes is proposed, following a comprehensive technical diagram for micromotion characteristic synthesis based on the wideband radar phase. Some restraint conditions for the resolution of the ambiguous phase are analyzed systematically according to the different characteristics of micromotion and radar parameters. The method provides a more precise tool to acquire the micromotion characteristic than the traditional Doppler frequency methods, and the experiments have shown the performances of wideband radar phase extraction and its ambiguity resolution.

Space time coding plays quite an important role in the design of multi‐input multi‐output (MIMO) radar. It has been shown that the correlation characteristics of waveform set have a great impact on the performance of target detection and localisation for MIMO radar. In this study, the target detection and localisation performance of co‐located MIMO radar is analysed theoretically and experimentally, when correlated (not ideally orthogonal or fully coherent) waveforms are transmitted. The relationship between transmit–receive beam pattern improvement and correlation coefficients of transmitted waveforms is investigated. The signal to interference and noise ratio varying with correlation coefficients in range‐Doppler matched processing is defined and analysed. Furthermore, Cramer–Rao bound for target direction of arrival estimation and target detection probability in the Neyman–Pearson criteria are derived when transmitting correlated waveforms. It is shown that the MIMO radar detection and localisation performance is much dependent with the correlation level of transmitted waveforms, which can be quite useful for MIMO radar orthogonal waveform design. Numerical simulation validates the theoretical analysis.

Multi‐input–multi‐output (MIMO) radar can fundamentally improve radar performance by using diversity technique. A group of specially designed signals, which usually are orthogonal, are required to be transmitted for MIMO radar obtaining excellent diversity performance. Although some well orthogonal codes have been provided, they are mostly Doppler sensitive, and their side‐lobes level also need to be improved. In this study, the poly‐phase codes model is presented and the optimisation problem is then analysed. An adaptive clonal selection algorithm is proposed to numerically optimise such poly‐phase coded orthogonal signals. To obtain low level of aperiodic autocorrelation side lobe and cross correlation as well as good Doppler shift tolerance, sustainable Doppler shifts are introduced into the optimisation course. Numerical simulation results show the superior correlation and Doppler tolerance performances comparing with some well known codes.

For maneuvering targets, the time-varying Doppler shifts will produce blurred inverse synthetic aperture radar (ISAR) images for a long coherent processing interval (CPI). By exploiting sparsity of the target scene, sparse recovery (SR) algorithms have been applied to achieve high cross-range resolution within a short CPI, during which the Doppler shifts nearly remain constant. For practical applications, however, the required pulse number for attaining an acceptable image is difficult to designate in various scenarios, and the common recovery procedure suffers from low efficiency because of having to solve a new SR problem from scratch when the new echo pulses are sequentially available. In this letter, we present a dynamic ISAR imaging algorithm based on sequential smoothed L0, which is proposed as an efficient recursive implementation of the SR approach. Furthermore, by defining the proper stopping rules, we can seek the optimal pulse number required in each CPI. Simulation results show that the proposed dynamic algorithm is more suitable for ISAR imaging of uncooperative targets.

There is an error in Fig. 3 in the above paper (ibid., vol. 10, no. 5, pp. 1041-1045, Sep. 2013). Bottom panels 3(g) and 3(h) are missing. The corrected figure is published here. We are sorry for the error.

To have a further understanding of the recently developed space-time adaptive processing (STAP) methods based on sparse representation (SR-STAP), this letter details the clutter sparsity observed by STAP radar systems. First, we review the principle and discuss the existing problems about clutter sparsity of the SR-STAP-type algorithms. Then, a theoretical analysis on clutter sparsity for a side-looking uniform linear array with constant pulse repetition frequency, constant velocity, and no crab is performed. Some important conclusions are obtained, and simulations are used to validate the correctness of them.

This paper presents a detection algorithm for micromotion targets, such as rotating antennas, in the synthetic aperture radar (SAR) geometry. It utilizes target's range cell migration characteristics, i.e. a micromotion target takes on a sine cure in the slowtime-range image, but with incontinuity or gaps when clutter (and part of target energy) is suppressed. Two-stage detection, including MTI and the extended Hough transform, is used to suppress clutter and to extract the sine curve. Quasi-real SAR data of Isleta Lake demonstrate high SNR gains and good detection performance.

Based on the compressed sensing (CS), we present a novel framework of moving target detection in random PRI radar. Firstly, the statistical characteristics of correlation output are analyzed to reflect the sidelobe pedestal. Then the equivalent sensing matrix is verified to approximately accord with the restricted isometry property by comparing it to a typical random CS matrix in a statistical sense. In order to cover the concerned range and velocity multi-channel processing is used. The simulation results demonstrate that this scheme has high performance of detection and large unambiguous scope, which can also shorten the coherent processing interval compared to traditional staggered PRI mode.

A novel algorithm is proposed for detecting and estimating vibrating targets in synthetic aperture radar (SAR) data based on a pulse-repetition-interval (PRI) transform. Azimuthal signals of vibrating targets can be modelled as sinusoidal frequency-modulated (SFM) ones. The algorithm utilises the resemblance between the Doppler spectrum of vibrating-target SFM signals (or ghost image) and a pulse train, and applies to the spectrum the PRI transform originally used for estimating PRIs of pulse trains. The algorithm can detect SAR vibrating targets under moderate signal-to-noise/clutter ratios, and is also capable of accurately estimating the vibration frequencies even if there are multiple targets in a single range cell. The algorithm proposed has been successfully applied to both simulated and quasi-real data, and compared with that of the autocorrelation method, showing its superiority.

The problem of target classification using inverse synthetic aperture radar (ISAR) images is studied under conditions of mass data processing, sparse scattering centre distribution, image deterioration and variation with the radar imaging view, all of which make target classification difficult. In this study, the authors propose a novel method based on combination of the feature space and the visual perception theory to achieve an accurate and robust classification of ISAR images. In order to make full use of local spatial structure information for classification, the local non-negative matrix factorisation (LNMF) is employed to construct an initial feature space, which is then optimised to calculate more discriminable feature projection vectors of each target. The approaches including speckle noise and stripes suppression, centroid and scale normalisation, LNMF, feature space optimisation with the maximum intersubject variation and minimum intrasubject variation and feature projection vectors calculation are detailed. Finally, the classification is performed with a k neighbours classifier. ISAR images used are obtained by range–Doppler imaging method with radar echoes of aircraft models generated by RadBase. Simulation results show a significant improvement on recognition accuracy and robustness of the proposed method.

This study considers the problem of jointly detecting whether a target is present in a scene and estimating its state, if it is there. This joint detection and estimation problem can be solved using a special case of the multi-target Bayes filter (referred to as the joint target detection and tracking (JoTT) filter). However, if the model used by the JoTT filter does not match the actual dynamics, the filter will tend to miss-detection directly or diverge such that the actual errors fall outside the range predicted by the filter's estimate of the error covariance. A similar difficulty arises, if the target behaviour can switch between different modes of operation, since the filter may then be accurate for only one particular mode. This study proposes a novel joint detection and tracking filter, which is the multiple model extension of the JoTT filter to accommodate the possible target manoeuvring behaviour. In addition, a sequential Monte Carlo implementation (for generic models) and a Gaussian mixture implementation (for linear Gaussian models) are proposed. The simulation results are presented to show the effectiveness of the proposed filter over the original JoTT filter.

This study proposes novel sparsity-aware space–time adaptive processing (SA-STAP) algorithms with L1-norm regularisation for airborne phased-array radar applications. The proposed SA-STAP algorithms suppose that a number of samples of the full-rank STAP datacube are not meaningful for processing and the optimal full-rank STAP filter weight vector is sparse, or nearly sparse. The core idea of the proposed method is imposing a sparse regularisation (L1-norm type) to the minimum variance STAP cost function. Under some reasonable assumptions, the authors firstly propose an L1-based sample matrix inversion to compute the optimal filter weight vector. However, it is impractical because of its matrix inversion, which requires a high computational cost when using a large phased-array antenna. In order to compute the STAP parameters in a cost-effective way, the authors devise low-complexity algorithms based on conjugate gradient techniques. A computational complexity comparison with the existing algorithms and an analysis of the proposed algorithms are conducted. Simulation results with both simulated and the Mountain-Top data demonstrate that fast signal-to-interference-plus-noise-ratio convergence and good performance of the proposed algorithms are achieved.

There are different kinds of multiple classifiers in complex recognition systems in pursuit of better recognition capabilities. To exploit the classifiers' potential as individual ones sufficiently and enable them to work cooperatively for the best classification results, they need to be considered as a whole and be dynamically managed according to the changing recognition occasions. In this paper, we present the conception of distributed Multiple Classifiers Management (MCM) and a self-adaptive recursive MCM model based on Mixture-of-Experts (ME). A control subsystem is consisted in the model, which allows the classification progress to be controlled by the systems' priori information when necessary. The model adjusts its parameters dynamically according to the current recognition state and gives the recognition results by combining the current individual classifiers' results with the previous combination result under priori information's control. An algorithm based on one step error correction is presented to acquire the model's parameters dynamically. It takes the previous times' ensemble classification results as true and corrects the current weights of the classifiers. At last, an experiment on the recognition of space objects is simulated. The experiment results show that the MCM model in this paper is effective for complex recognition system containing heterogeneous classifiers on improving the recognition rate and robustness.

The purpose of this letter is to investigate the sensitivity relative to the antenna position uncertainties (APUs) for multiple-input multiple-output (MIMO) radar with colocated antennas. Manifold study is introduced to MIMO radar virtual array as an analyzing tool. To assess the importance of each antenna in a MIMO radar system, we extend Manikas's sensor importance function to MIMO radar. Furthermore, to compare the robustness to APUs of different antenna geometries, we extend the overall-system-sensitivity criterion to MIMO radar system. We show that, with the same sensor geometry, MIMO radar has better robustness performance than the corresponding phased array because of waveform diversity.

In inverse synthetic aperture radar (ISAR) imaging of targets with complex motion such as the high maneuvering airplanes and fluctuating ships with oceanic waves, the azimuth echo signals can be modeled with cubic chirps after translational motion compensation, and then, the azimuth focusing quality will be deteriorated by the time-varying chirp rate. In this paper, a parameter estimation method of cubic chirps is proposed based on the discrete chirp Fourier transform (DCFT), which is generated from DCFT for quadratic chirps. Several properties of DCFT for cubic chirps are derived, and we show that the modified DCFT (MDCFT) is more appropriate to deal with the practical applications (e.g., ISAR imaging) than the original DCFT. Therefore, we put forward the imaging algorithm based on MDCFT, and then, simulation results confirm the validity of the proposed algorithm.

Fast raw-signal simulation is of considerable value for missile-borne synthetic aperture radar (SAR) algorithm development. On the basis of the two-dimensional (2-D) Fourier simulation method for stripmap SAR, we present a fast echo simulation method suitable for missile-borne SAR diving with constant acceleration. The analytical expression for the 2-D signal spectrum is derived and then converted to a stripmap one. Simulation results for a point target and a real scene demonstrate its validity and effectiveness.

Targets with uniform rotational motion may cause migration through cross-range cells during the imaging time, which makes the inverse synthetic aperture radar image smeared. Traditional motion compensation methods hardly work well because it is difficult to estimate the quadratic phase error (QPE) caused by uniform rotational motion efficiently. To solve this problem, a novel QPE estimation method based on phase cancellation (PC) is proposed in this letter. In this method, PC is used to eliminate the linear term of the phase, which is a disadvantage to the QPE estimation. By employing the weighted linear least squares algorithm, the QPE can be estimated efficiently and robustly. Experiments with both simulated and measured radar data demonstrate the performance of the proposed method.

Fast rotating targets such as gimbaled antennas or propeller blades may cause migration through resolution cells (MTRC) of the high-resolution range profile during the imaging time, which makes the inverse synthetic aperture radar image smeared. To solve this problem, a novel imaging method for fast rotating targets is proposed in this paper. The method is based on the segmental pseudo Keystone transform, which is designed to realize MTRC correction. The fast realization is achieved by employing the discrete match Fourier transform, which makes the algorithm feasible and simple. Experiments with simulated radar data demonstrate the performance of the proposed method.

This paper analyzes the influence of typical target micromotions on synthetic aperture radar (SAR) images, azimuth resolution limit, SAR/ground moving target indication (GMTI), and MTI. According to the micromotion periods contained in the coherent processing interval, a new range model expansion and a generalized paired echo principle are proposed and applied to underlie the analysis. Several new kinds of image characteristics including gray strips, ghost points, and fences are reported, which are sheerly distinct from those of slow movers. Micromotion will also cause a prominent range cell migration even if its amplitude is far smaller than the range resolution. SAR/GMTI and MTI techniques will, in general, become invalid for micromotion targets. The influence is eventually demonstrated by the simulated data in the airborne single-channel geometry, and it can be used for SAR image interpretation as well as passive jamming.

Enhancement of vessels in medical images is still an unsolved problem. Multiscale approaches were proposed to improve the vessel enhancement effect based on the structure size and image resolution. Vessel enhancing diffusion (VED) filter is one of the multiscale approaches, which was based on the scale space theory. VED performs well on enhancing vessel structures but cannot preserve complex structures such as the vessel junctions. In this paper, a structure-preserving diffusion tensor is defined in the diffusion equation, which brings a structure-preserving vessel enhancing diffusion filter. Through the multiscale framework, the proposed method enhances the vessel structures especially the complex structure such as junctions. Experimental evaluation performed on various vessel data sets demonstrated the effectiveness of the proposed method.

The formulation of a point target spectrum is a key step in deriving synthetic aperture radar focusing algorithms, which exploits the processing efficiency of the frequency domain. However, the existence of a double-square root in the bistatic range equation makes it difficult to find an exact analytical solution for the 2-D spectrum. In this letter, according to the idea of function optimal approach, we derive a new 2-D point target spectrum on the basis of Legendre polynomial expansion, which is more exact than the existing spectra during the synthetic aperture time.

The range Doppler algorithm (RDA) is based on Taylor series expansion for the transfer function (TF) phase component, resulting in increasing phase error as the range frequency or the squint angle increases. We introduce Legendre orthogonal polynomials into synthetic aperture radar data processing steps to replace Taylor series expansion used in approximating the TF phase. A novel range Doppler imaging approach based on Legendre expansion is addressed with an extended RDA as example, and the analytical expressions of the new phase multiplication factors are then derived. The simulation results show that the proposed method provides better focusing performance than the conventional RDA in the same squint case and is more suitable for the large squint mode while there is negligible increment of computation load.