In accord with imaging mechanism, by tackling the integrated quantum circuit without multiple preparations and measurements, a quantum algorithm with remarkable speedup for synthetic aperture radar (SAR) imaging is proposed to decrease the data storage and time consuming in this letter. First, the quantum algorithms for 2-D matched filtering and range cell migration correction (RCMC) are presented. Then, the relevant quantum circuits are designed and bonded together as an integrated quantum circuit for SAR imaging. It would be further applied to wide-swath imaging and sparse-driven radar imaging to form the corresponding quantum algorithm from quantum data to quantum data. The polynomial speedup in computational complexity is analyzed theoretically, and the needed quantum data are reduced exponentially compared to classical data. Experimental results obtained by computer simulation of quantum algorithm demonstrate that the proposed method can obtain the high-resolution SAR image and decrease at least$10^{2}$orders in complexity.

The significant domain differences between heterogeneous Synthetic Aperture Radar (SAR) data lead to a serious degradation of the generalization ability of existing detection models in real cross-source scenarios. Traditional domain adaptation methods rely on target domain data and are difficult to cope with multi-source dynamic interference, while existing domain generalization (DG) schemes have not yet adequately addressed the multi-dimensional domain shift coupling problem unique to SAR images. This letter presents a unified framework for cross-scenery DG heterogenous SAR image object detection task. First, we match the SAR image scattering features with dynamic statistics to expand the domain feature space. Next, domain-invariant features and domain-specific features disentanglement module is designed for the above feature space, and redundant domain discriminative information is removed. In this way, the performance enhancement of both for DG algorithm can be maximized. Cross-source scenarios object detection results across multiple SAR/remote sensing image datasets obtained in a detection head with Faster RCNN as the baseline network all achieve the state-of-the-art performance available.

Stable and efficient inverse synthetic aperture radar (ISAR) imaging methods for micro-motion targets remain a challenging problem. The approximation accuracy of traditional low-rank constraint solving methods based on nuclear norm is required to be improved in high-resolution ISAR imaging and it cannot be applied to large matrices due to the need for singular value decomposition (SVD). In this letter, we replace the nuclear norm constraint with the factor group-sparse regularization (FGSR) without SVD and propose a micro-motion target ISAR imaging method based on FGSR low-rank representation, which improves the imaging accuracy and has a computational efficiency gain of about 5%. Experiments on simulated and measured data verify the effectiveness of the proposed method.

ISAR imaging for maneuvering targets (MT) in sparse aperture (SA) condition is a challenging problem. Range instantaneous Doppler (RID) is useful for ISAR imaging of MT through time-frequency analysis. However, the performance of RID deteriorates in SA, and frequency resolution is limited by the assumption of stationary signal in the time window. To tackle these issues, a complex value parametric efficient sparse Bayesian learning (CPESBL) ISAR imaging algorithm is proposed in this letter. In our algorithm, the one frame signal of MT ISAR imaging is modeled as the multicomponent Chirp signal. This model is solved by CPESBL which contains the complex value efficient SBL (CESBL) with low computational complexity and the Quasi-Newton method estimating the Chirp rate parameter. Then the focused ISAR image can be obtained efficiently. Moreover, a dimension shrinkage strategy is also proposed to further improve the computational efficiency considering the continuity of sequential ISAR images. With a low computational complexity, the proposed algorithm achieved the best image quality index both in simulated and measured data experiments.

Currently, the sparse imaging problem of synthetic aperture radar (SAR) is primarily addressed by compressed sensing (CS) theory, which introduces prior information into image restoration tasks via regularization. However, simple regularization constraints cannot provide the complex structural information of targets, and their denoising performance is unsatisfactory at low signal-to-noise ratios (SNRs) and sampling rates. In this letter, a novel sparse SAR reconstruction network is proposed based on plug-and-play (PnP) and approximated observation. First, a chirp-scaling algorithm (CSA) operator-derived approximated observation model is applied to reduce computational costs. Then, this sparse imaging problem is iteratively solved with a matched filter-based approximate message-passing (MFAMP) method. To overcome the limitations of prior models in existing sparse imaging methods, a PnP prior model is incorporated within the sparse reconstruction framework instead of using the ℓ1sparse regularizer. Finally, the solution procedure is unfolded as a deep imaging network, dubbed as PnP-MFAMP-Net. Experimental results validate its robustness and superiority. Even at a sampling rate of 25%, PnP-MFAMP-Net can achieve a PSNR gain of approximately 15 dB compared to the AMP-Net.

This letter proposes a novel open set domain adaptation method for heterogeneous synthetic aperture radar (SAR) image classification. In this problem, the test data often differ from the distribution of training data and may contain unseen classes that are not present in the training data. Considering the inaccuracy of existing studies in classifying samples of unknown class in target domain, we propose a dynamic self-training network structure to distinguish known classes from unknown class by dynamically adjusting the discriminant threshold. Then in order to further improve the accuracy, a self-training domain-specific weak alignment module is designed, which abandons the previous idea of hard aligning source and target features, and achieves the weak alignment of target domain by the idea of prototype normalization and domain-specific knowledge extraction. Finally, experiments on two heterogenous SAR image benchmark datasets show that the proposed method improves the classification accuracy by more than 3% over the state-of-the-art methods.

Due to the lack of color and texture information and the fuzzy boundary of infrared (IR) small targets, the pixel-level mask annotation process consumes a lot of manual cost and is difficult to achieve accurate annotation. To further reduce the annotation burden, we propose an IR small target mask generation algorithm based on single-point supervised multi-scale chain growth clustering (MCGC). The core of this work is the adaptive generation of IR small-target pseudo mask maps under the supervision of randomly given single-point labels, sequentially through the strategies of multi-scale chain growth, Euclidean coefficient decay, K-Means clustering, and eight-neighborhood clustering. On the four public datasets, ablation experiments, qualitative and quantitative comparison experiments demonstrate that the MCGC algorithm has an efficient and accurate IR small target pseudo mask generation capability, which can be adapted to different numbers, scales, shapes, and intensities of targets in complex backgrounds. In addition, IR-Labelmask, an IR small target mask annotation software designed based on the MCGC algorithm, is publicly available on kourenke/IR-Labelmask-software (github.com). To our knowledge, this is the first mask annotation software designed for IR small target.

As a result of residual phase error caused by motion parameters, ground moving targets (GMTs) are defocused and displaced in conventional synthetic aperture radar (SAR) imaging. Although some refocusing algorithms for GMT have been proposed, these methods are difficult to handle geometric correction and sparse recovery in squint mode simultaneously. Deep learning (DL) technology has been successfully used to solve radar imaging problems in microwave vision, where deep unfolding networks (DUNs) and convolutional neural networks (CNNs) are the most widely applied. In this article, a novel GMT imaging network (GMTIm-Net) is proposed for squint SAR and sparse sampling, whose framework combines DUN and CNN advantages. Specifically, we first incorporate a matched filter-based approximated observation model and a minimum entropy-based motion parameter estimation method within a sparse reconstruction framework. An iterative shrinkage threshold algorithm is adopted to solve this framework, and the solution procedure is unfolded as a GMT refocusing network. Then, we introduce plug-and-play (PnP) technology to replace the$\ell _{1}$norm-based regularizer for improving its noise immunity. Finally, a CNN-based image transformation network is proposed to perform geometric correction of imaging results in squint mode. By inputting the 2-D sparse complex-valued GMT echo, the trained GMTIm-Net can efficiently output focused and corrected GMT images. The experiments demonstrate that our proposed GMTIm-Net outperforms conventional GMT focusing methods in terms of focusing performance and computing efficiency.

Wideband waveform design of MIMO radar with high resolution in complex electromagnetic environment is a challenging problem. To address the issue, this letter proposes a waveform optimization method based on multiple criteria, including beam pattern formation, frequency-space power distribution (FSPD) matching and discrete ambiguity function (DAF) shaping. Considering the constant modulus constraint and range-Doppler resolution requirement, the continuous phase code with a "thumbtack" ambiguity function (AF) is selected as the waveform form to be designed, and then the waveform phase matrix becomes a variable to be optimized. Suppose a multi-target and bandwidth resource sharing scenario, under the premise of signal time-domain discreteness, both the space and bandwidth are divided into many discrete units, a time-frequency-space joint optimization model with respect to the waveform phase matrix is established, so as to realize the beam pattern, FSPD and DAF in compliance with expectations. Furthermore, aiming to solve the non-convex optimization problem, a calculating approach based on conjugate gradient (CG) method is employed to achieve the closed-form solution of the multidimensional variable after simplifying the objective function. Compared with the traditional methods, the experiments verify that the proposed method can not only approximate the required multi-beam shape and FSPD, but also have good range-Doppler tolerance and sensitivity.

In micromotion signature analysis, the radar return signal with missing sampling may cause defocused time–frequency representation (TFR) and thus prevent micromotion characteristics acquisition. To address the issue, we present an adaptive time–frequency distribution reconstruction method based on$L_{1}$regularization. First, the$L_{1}$regularization is expressed as a combination of two$L_{2}$regularizations based on Hadamard product parametrization. Then, the iterated Tikhonov regularization is applied to solve each$L_{2}$regularization alternatively. Moreover, the regularization parameter is updated adaptively based on the matching pursuit principle at each iteration. Finally, the reconstructed TFR is updated based on least-square-error criterion to eliminate the attenuation of signal amplitude. Simulation and measurement data examples have demonstrated the effectiveness of the method.

Traditional synthetic aperture radar (SAR) target detection methods use matched filtered SAR images as input, and the detection performance is restricted due to the high sidelobes and speckle noise of these images. Sparse SAR imaging methods developed in recent years provide the advantages of reducing sidelobes, noise, and clutter. The imaging results obtained with these methods could help improve the SAR target detection performance. In this article, to improve the target detection performance using sparse SAR images as input, we proposed a convolutional sparse feature enhancement method to meet the needs of Bayesian saliency detection. The proposed Bayesian saliency joint target detection method comprised the following three steps: first, to obtain sparse SAR images with continuous contours and fewer holes in the target area, we proposed a convolutional L1 sparse regularization method. Second, a regularization parameter optimization method was derived to quickly obtain optimal regularization parameters for saliency detection. Finally, target detection results were obtained through a superpixel-based Bayesian saliency joint detector. Extensive experiments verified that the proposed method could improve the SAR target detection accuracy in complex backgrounds.

High-resolution range profile (HRRP) sequences have great potential for space target classification because they can provide both scattering information and micromotion information. However, many factors cause an obtained HRRP sequence for a space target to be corrupted in real cases due to noise interference, limited radar resources, and the requirement of multitarget observations. Many space target classification methods cease to be effective when HRRP sequences are corrupted, so classifying space targets with corrupted HRRP sequences is still a challenging problem. To solve this problem, a novel space target classification method based on a temporal–spatial feature aggregation network (TSFA-Net) is proposed by using the corrupted HRRP sequences directly. First, a sequence-to-token module (S2T-module) is designed to extract low-level and fine-grained features from the raw inputs. Second, to effectively model the long-range dependencies among corrupted HRRP sequences and capture global representations without losing target local features, we propose a parallel and dual-branch block, i.e., a temporal–spatial feature aggregation block (TSFA-block), by combining a Transformer network and a convolutional neural network (CNN). Then, via progressively hierarchically stacking TSFA-blocks, a hierarchical temporal–spatial feature aggregation subnetwork (H-TSFA-subnetwork) is constructed to obtain the final temporal–spatial features. Finally, a token-to-label module (T2L-module) is adopted to obtain the classification results. Extensive experiments demonstrate that the proposed method achieves state-of-the-art classification accuracy for space target classification with HRRP sequences, especially under the conditions of a low signal-to-noise ratio and a high missing rate.

Aiming at the problem of ship classification in Synthetic Aperture Radar (SAR) images crossing different imaging satellites, we propose a novel domain adaptation (DA) network. For the proposed DA network, we consider one labeled SAR image dataset as source domain and another unlabeled dataset acquired by a different imaging satellite as target do-main. First, the structural regularization of the source domain is achieved by jointly training the feature classifier and the domain classifier. Then, by minimizing the KL-divergence between the label distribution predicted by the network and the introduced auxiliary distribution, the cluster alignment of the target domain is further realized. The experimental results on the datasets obtained from different satellites verify the performance of proposed method is better than state-of-the-art DA method.

The sparse-driven radar imaging can obtain the high-resolution images about target scene with the down-sampled data. However, the huge computational complexity of the classical sparse recovery method for the particular situation seriously affects the practicality of the sparse imaging technology. In this paper, this is the first time the quantum algorithms are applied to the image recovery for the radar sparse imaging. Firstly, the radar sparse imaging problem is analyzed and the calculation problem to be solved by quantum algorithms is determined. Then, the corresponding quantum circuit and its parameters are designed to ensure extremely low computational complexity, and the quantum-enhanced reconstruction algorithm for sparse imaging is proposed. Finally, the computational complexity of the proposed method is analyzed, and the simulation experiments with the raw radar data are illustrated to verify the validity of the proposed method.

When echoes of micromotion targets are overlapping in the time-frequency (TF) domain and sampling data are missing, the decomposition of the multicomponent micro-Doppler (m-D) signals is challenging. To address this issue, this letter proposes a method for multicomponent m-D signal decomposition by iterations of the instantaneous frequencies (IFs), individual components, and complex envelopes. To initialize the IFs, the well-focused time-frequency representation (TFR) is obtained by sparse reconstruction of the incomplete data, and then the IFs of the TFR can be estimated by the short-time variational mode decomposition (STVMD) algorithm. After initialization, the IFs, individual components, and complex envelopes are updated by the intrinsic chirp component decomposition (ICCD), alternating direction method (ADMM) of multipliers, and least-square-error criterion (LSEC), respectively. Finally, the proposed method is verified by simulation and application to real data.

Multi-component nonlinear chirp signals (NCSs) widely exist in microwave remote sensing. In some applications, it is necessary to separate NCSs containing close components in the time-frequency (TF) domain. However, the phase-corrupted data may cause a defocused TF signature and prevent individual component extraction. To solve the problem, the optimization model is developed to reconstruct and decompose multi-component NCSs with phase errors and solved by an alternating iterative algorithm. In each iteration, the individual components, phase errors, and the regularization parameter are updated by the alternating direction method of multipliers, least-square-error criterion, and the matching pursuit principle, respectively. Finally, the effectiveness of the proposed method is verified by simulation and real data examples.

In micromotion feature extraction, the incomplete and phase-corrupted radar echo may cause bad time–frequency representation (TFR) and prevent micromotion feature extraction. To solve the problem, we establish a sparse regularization model to reconstruct well-focused TF distributions. The regularization model is solved by the iterative soft-thresholding algorithm (ISTA). In each iteration, the hard thresholding function and least-square-error criterion are developed to estimate the phase errors. For micro-Doppler signal real-time processing to save radar time resources, the received signal can be directly sparse recovered in real time rather than waiting for the complete signal. Finally, the effectiveness of the proposed method is validated by the simulation results.

Envelope alignment is one of the key steps for inverse synthetic aperture radar (ISAR) translational compensation. The traditional envelope alignment method cannot be accurately completed under a low signal-to-noise ratio (SNR), which will limit the accuracy of subsequent phase focusing. We propose a deep recurrent neural network (RNN) frame to address the problem. This is an end-to-end learning approach. Radar echo pulses are input to the network one by one according to time sequence. The inputs of each layer can be divided into two parts. The one is the current pulse, and the other one, named “state,” is the outputs of the previous layer except for the aligned pulse. Moreover, the outputs of each layer contain the “state” for the next layer and the aligned result of the input pulse. The above structure is a typical RNN, and the “states” transform the time-sequence information between different pulses. Compared with the traditional methods, the experiments verify that the proposed network can not only provide better alignment accuracy under low SNR but also require a shorter alignment time.

Compressed sensing (CS) reconstruction of nonsparse scenes is one of the difficulties in synthetic aperture radar (SAR) imaging technology. Although the conventional CS method with sparse representation has proven applicable for nonsparse SAR reconstruction, its disadvantages are unsatisfactory imaging quality and high computational complexity under downsampling. In this paper, a novel deep learning approach for nonsparse SAR scene reconstruction is proposed based on sparse representation and the iterative shrinkage threshold algorithm (ISTA). Specifically, we first develop a sparse representation-based imaging model associated with the ℓ1sparse regularizer in nonlinear transform domains. Then, the advantages of the recurrent neural network (RNN) and convolutional neural network (CNN) are incorporated into an ISTA-inspired deep unfolded network (DUN) called SR-ISTA-Net, in which all the parameters are layer-varied rather than handcrafted. The experiments verify that the proposed SR-ISTA-Net can provide high-quality reconstruction results under nonsparse scenes while substantially reducing imaging time.

Synthetic aperture radar (SAR) image classification task when the training and test sets have different distributions can be initially solved using existing domain adaptation (DA) methods. However, considering that none of their classification accuracy is high, this letter proposes an active learning DA classification method to further solve this task. First, an adversarial learning-based DA pipeline is put forth, using labeled source and unlabeled target domains to conduct adversarial learning in order to narrow the domain gap. A prototype regularization process is then built, which further enhances the target domain data clusters’ ability to discriminate between them. In order to fully improve SAR image classification accuracy, we then propose a dynamic hard sample selection process to choose hard samples to supplement into the subsequent stage of training samples. This process involves moving the gradient direction of the query function closer to the gradient direction of the class margin objective function. Extensive experiments on SAR image datasets with different distributions from different imaging platforms and optical remote sensing datasets have verified the effectiveness and superiority of the proposed method.

For synthetic aperture radar (SAR) image classification problem, we need to take into account unlabeled datasets containing unknown classes crossing different satellites. In this letter, a spherical space domain adaptation (DA) network under open set condition is proposed to solve this problem. First, we transform the prior Euclidean feature space into the spherical space to construct a classification network such that features of the same class of SAR images are clustered together and features of different or unknown classes are separated on the hypersphere. Second, a correction module is designed to increase the accuracy of the pseudo-label obtained by the classifier. Then, based on the adversarial learning strategy, we introduce the gradient alignment module to achieve better alignment of the source and target domains. Finally, tests on two SAR benchmark datasets from distinct satellites show that the proposed network outperforms state-of-the-art (SOTA) approaches in terms of classification accuracy.

Inverse synthetic aperture radar (ISAR) can only provide two-dimensional (2-D) images to represent the target’s scattering projection on the corresponding imaging planes. However, as the echo of the target from different observation angles can be achieved simultaneously, the radar network can provide 3-D scattering information about the target. In this article, a novel 3-D scattering image reconstruction method is proposed based on the radar network and compressed sensing (CS). First, the general signal model and the reconstruction conditions of the radar network 3-D reconstruction are given. Then, the 3-D scattering distribution reconstruction model is built in a CS framework, which can reconstruct the positions and coefficients of scattering centers simultaneously. Moreover, the sparse structure with three layers in radar network 3-D reconstruction is defined and a fast 3-D reconstruction algorithm is proposed. To the end, the numerical simulations under noise scenarios and the principle prototype experiments on real data are shown to demonstrate the validity of the proposed method.

Compressed sensing (CS) based synthetic aperture radar (SAR) imaging methods are showing superior potential in imaging performance over classical matched filtering based methods. However, the CS-based methods require much more computational cost to solve the iterative optimization composed of large-scale matrix operators. To hold the improvement of imaging performance and reduce the computational cost, in this paper, we propose a novel SAR imaging method by Deep Unfolded Network (DUN) of Iterative Shrinkage Threshold Algorithm (ISTA) with the approximated observation of Range-Doppler Algorithm (RDA) operator. The proposed method takes the radar echoes as the input to learn the imaging procedure. Firstly, the approximated observation is utilized in SAR imaging model to reduce the size of the DUN. Moreover, we use ISTA as an example to introduce how to establish DUN with approximated observation, in which the detailed structure to handle the complex-valued radar echoes is also designed. Finally, the auto-encoder is utilized to calculate the difference of the echoes rather than the imaging results so that we can train the proposed network by unsupervised learning. The experiments of both point targets, surface targets, and real scenes show that the proposed imaging method is superior in terms of imaging performance and computing efficiency.

Except for the slight difference of micromotion parameters, some decoys and warheads have the same geometry and micromotion form. As a result, recognition of similar space cone–cylinder targets is one of the difficult problems in ballistic target recognition. In recent years, due to the good effect of deep neural networks (DNNs) in optical target recognition, space cone–cylinder target recognition methods based on DNN have attracted wide attention. However, these DNN-based methods only recognize the space cone–cylinder targets with different shapes and different micromotion forms. Moreover, these methods require some time-consuming preprocessing operations, which need to observe the target for at least one micromotion period. To recognize similar space cone–cylinder targets, we propose a complex-valued coordinate attention networks (CV-CANets)-based end-to-end recognition method. Firstly, we establish the signal model of space cone–cylinder targets. Secondly, we propose CV-CA blocks by transforming the coordinate attention mechanism into the complex-valued domain. Then, we construct CV-CANet based on the proposed CV-CA blocks. Finally, the proposed CV-CANet is trained and tested by the narrowband radar echo data, which is generated by electromagnetic calculation. Compared with the convolutional neural network (CNN)-based recognition methods, the proposed method can not only recognize the similar space cone–cylinder targets but also is superior in terms of time cost and observation requirement. Extensive experiments validate that the proposed recognition method is effective when the targets only have a slight difference on the precession angular frequency and the observation time is less than half a period.

In the face of Synthetic Aperture Radar (SAR) image object detection with different distributions of training and test data, traditional supervised learning methods cannot achieve good detection performance. Domain adaptation (DA) method has been shown to have the ability to solve this problem, but existing DA object detection algorithms all use adversarial DA theory for the detection task, which is ineffective in solving object regression localization in the detection task. In this article, to better solve the above problem, an automatic SAR image ship detection method based on feature decomposition crossing different satellites is proposed. The feature extraction layer of backbone network is divided into low level and high level, where domain-invariant feature extractors are designed for the local features extracted from the low level and the global features extracted from the high level, respectively. We argue that the local and global features extracted from source domain and target domain contain domain-specific features (DSF) for adversarial DA and domain-invariant features (DIF) that contribute to object regression localization. Then, we decompose the local features and global features into DSF and DIF via vector decomposition method. For DSF counterpart, we introduce adversarial DA attention for feature alignment. DIF from the local features are fused into the backbone network for high-level global feature extraction. Finally, by using region proposal network and adversarial domain classifier, we can get the accurate bounding box and object class of SAR image objects. Extensive experiments prove that the proposed method outperforms state-of-the-art methods in terms of detection performance.

Traditional deep learning Synthetic Aperture Radar (SAR) image object detection methods fail to provide effective detection results when faced with SAR image datasets with different joint probability distributions obtained from multiple imaging satellites. In this article, an automatic SAR image object detection method based on domain adaptation is proposed to adapt to unlabeled target domain datasets acquired by different satellites. On the basis of introducing an adversarial domain adaptation learning strategy, we propose Adversarial Learning Attention (ALA) and Compensation Loss Module (CLM) on the baseline network. In ALA, considering the great difference in the scattering intensity of SAR images, the entropy vector can be used to distinguish the high-entropy and low-entropy regions among them and assign the corresponding weights, based on which the adversarial domain adaptation learning attention is proposed to achieve instance-level feature alignment and pixel-level feature alignment in source domain and target domain, respectively. In CLM, the domain alignment of pixel-level feature and instance-level feature of SAR image objects is first implemented, and then to make the feature alignment of both domains more accurate, better aggregation of proposals of different classes of prototype objects in the same domain is required, and further compensation loss is proposed to further restrict the prototype alignment of SAR object features in both domains. We conduct experiments on two SAR datasets obtained from different satellites whose results show the superiority of the proposed method over the state-of-the-art (SOTA) methods and the effectiveness of the proposed module in improving the detection accuracy.

Digital false-target image synthesizer (DIS) has been proposed as a deceptive jamming method against inverse synthetic aperture radar (ISAR). The DIS method based on sub-Nyquist sampling jamming has attracted increasing attention in recent years. However, multiple false-target images generated by sub-Nyquist sampling jamming are distributed in the down-range direction rather than the 2-D range-Doppler (RD) imaging plane. A novel DIS method is proposed in this letter. First, an additional phase controlled by polyphase code is added to the intercepted pulse, and the multiple false-target images will be induced regularly along the cross-range direction. Second, in order to generate false-target images both along the down-range and cross-range directions, the sub-Nyquist sampling theory is applied to the first step. The advantage of the proposed method is implementation simplicity. Experimental results verify the effectiveness of the proposed method.

Conventional single antenna synthetic aperture radar (SAR) has a conflict between wide imaging swath width and fine cross-range resolution. Single input multiple output (SIMO) and multiple input multiple output (MIMO) radars are the two methods to overcome this limitation. In order to separate the signals of MIMO radar, the codes are expected to be orthogonal for all delays. This is impossible in SAR. Due to the special imaging geometry of SAR, short-term shift orthogonal (STSO) codes were proposed to be used in MIMO SAR. This letter compares the SIMO SAR structure and MIMO SAR structure using STSO codes. We draw a conclusion that the number of receivers of MIMO structure is less than that of SIMO structure.

Based on the narrowband radar, an imaging method for space targets is proposed in this letter, which is named as single-range interferometric imaging. First, the theory of space target echo signal interferometric processing in narrowband radar is explained. Then, through conducting short-time Fourier transform on the echo signal received by three antennas, the curves on time-frequency plane correspond to different scatterers are effectively extracted and separated and the interferometric phase of different scatterers is obtained. Finally, 2-D imaging for space target is realized. Compared to existing methods, only a single multiantenna radar is needed to obtain the 2-D image of target with accurate scaling result. The simulation results under different occasions have confirmed the effectiveness of the proposed method.

Traditional micro-Doppler (m-D) analysis theories largely focus on isolated targets, making these theories difficult to utilize in monitoring and recognizing space group targets. This letter proposes an algorithm for separating ballistic group targets based on the extraction of micromotion features. Modeling the ballistic targets as cone-cylinder models, the skeleton extraction method in morphology image processing is first utilized to suppress the sidelobes of range profiles. A sliding window whose length of frames can be adaptively changed according to the curve characteristics is established to separate the m-D curves. Then, different recording criteria are adopted considering different types of intersections. After separating the m-D curves, the group targets are distinguished by extracting the different tendency gradients of group targets. In addition to being capable of distinguishing group targets, the proposed algorithm is robust to influences from noise. Simulations are performed to validate the effectiveness of the proposed method.

The precession feature of space target provides an effective approach for target recognition. However, with the requirements that target detection and tracking to be completed successfully before the processing of precession features extraction can be implemented, the existing methods demand radar resources allocation for target detection, tracking and feature extraction, respectively, thus reducing the radar efficiency. In this paper, by establishing a feedback loop between precession feature extraction and TBD (track before detect) of target, a cognitive feature extracting method is proposed. And the sliding-type scatterer model is used for describing rotationally symmetric target. With the proposed method, the precession feature parameters of target can be extracted concurrent to implementing the target detecting and tracking. Simulation results show the effectiveness of the proposed method.

This paper focuses on the problem of maritime surveillance for ship detection and proposes a novel monostatic SAR high resolution wide swath imaging scheme. The scheme changes the way that Scan SAR illuminates sub-scenes, and proposes a scanning mode using the prior information of ships' spatial distribution in each sub-scene. Firstly, a method based on range profiles is used to obtain the target number in each sub-scene. After that, the radar illuminates each sub-scene in a probability calculated by a transfer-probability matrix. Finally, a compress sensing algorithm is utilized to reconstruct each sub-scene using the obtained sparse aperture echo data, and the whole wide swath image is joint by putting all the sub-scenes together. Experimental results show that the proposed imaging scheme can get a wide swath image effectively without reducing the image resolution.

Inverse synthetic aperture radar sparse imaging based on compressive sensing has been widely researched. The measurement matrix significantly affects the performance of target imaging. In this letter, focus on the kind of signals that consist of several subpulses with stepped frequency, a measurement matrix optimization method based on genetic algorithm (GA), is proposed. The actual physical observation process is considered and the target characteristics are utilized to optimize the measurement matrix. Then, the expected imaging results can be obtained with minimum data using the optimized measurement matrix. Meanwhile, the orthogonal matching pursuit algorithm is improved for signal reconstruction, which can reduce the computation load significantly. The effectiveness of the proposed method is demonstrated by experiments.

The combination of frequency-modulated continuous wave (FMCW) technology and synthetic aperture radar (SAR) leads to lightweight, cost-effective, and low-power dissipation imaging sensors of high resolution. For extracting the features of vibrating targets on ground in FMCW SAR, the displaced phase center antenna technique is introduced into the FMCW SAR system to suppress the ground clutter, and then, the signal characteristic is analyzed. It indicates that the energy of the joint time-frequency distribution is presented as an uneven distribution that is induced by a slow time envelope (STE). In this letter, a novel extracting method of vibrating features based on STE signatures is proposed. With this method, the vibrating frequency and amplitude can be calculated from some extracted positions in the STE. Some simulations are given for validating the feasibility and effectiveness of the method.

The micromotion of a target will generate a micro-Doppler (m-D) effect in the frequency domain. The m-D effect is regarded as a unique property of the target, which has special significance in target detection, identification, and classification. The classical range-Doppler algorithm cannot obtain a clear inverse synthetic aperture radar (ISAR) image due to the m-D effect induced by micromotion dynamics. The m-D effect induced by periodical micromotion is represented as a sinusoidal modulation in a spectrogram, whereas the Doppler induced by a main body is depicted as the form of a straight line. Therefore, the extraction of an m-D signature is transformed into the separation of a sinusoid and a straight line. The cancellation technique is a classical method for removing ground clutter. Based on the same principle, the cancellation technique is applied to the spectrogram in this letter, which successfully achieves the separation of the m-D signature and gets the clearer ISAR image of the main body. The effectiveness and robustness of the algorithm are proved by simulation results.

Imaging an avian target by inverse synthesis aperture radar (ISAR) is a novel and important technological approach of solving the problem of avian detection. However, the ISAR images of birds obtained with the conventional range-Doppler algorithm could be contaminated due to serious micro-Doppler effects, which are generated by the birds' flapping wings. In this letter, a novel imaging method of birds is proposed, which is simple to comprehend and operate, and avoids lots of complications and computation burdens. In the method, the moving status of bird is identified first via finding the variety of moving average values of the cross-correlation coefficient of the adjacent high-resolution range profiles. The usage of moving average values is attributed to the characters of the bird's flapping. The parts of respective flapping spectrogram can then be eliminated, and the parts of the residual spectrogram, i.e., the respective gliding spectrogram, can be connected to prepare for the cross-compression. In this letter, the minimum waveform entropy criterion and genetic algorithm are employed in the spectrogram connection to compensate the phase error. Finally, the feasibility and effectiveness of the methods are verified by simulation results.

The micro-Doppler (m-D) effect induced by the rotating parts or vibrations of the target provides a new approach for target recognition. To obtain high range resolution for the extraction of the fine m-D signatures of an inverse synthetic aperture radar target, the stepped-frequency chirp signal (SFCS) is used to synthesize the ultrabroad bandwidth and reduce the requirement of sample rates. In this paper, the m-D effect in SFCS is analyzed. The analytical expressions of the m-D signatures, which are extracted by an improved Hough transform method associated with time-frequency analysis, are deduced on the range-slow-time plane. The implementation of the algorithm is presented, particularly in those extreme cases of rotating (vibrating) frequencies and radii. The simulations validate the theoretical formulation and robustness of the proposed m-D extraction method.

Curvelet transform is one of the recently developed multiscale transform, which can well deal with the singularity of line and provides optimally sparse representation of images with edges. But now the image denoising based on curvelet transform is almost used the Monte Carlo threshold, it is not used the feature of imagespsila curvelet coefficients effectively, so the best result can not be reached. Meanwhile, the wavelet transform codes homogeneous areas better than the curvelet transform. In this paper a joint multiscale algorithm with auto-adapted Monte Carlo threshold is proposed. This algorithm is implemented by combining the wavelet transform and the fast discrete curvelet transform, in which the auto-adapted Monte Carlo threshold is used. Experimental results show that this method eliminate white Gaussian noise effectively, improves Peak Signal to Noise Ratio (PSNR) and realizes the balance between protecting image details and wiping off noise better.

The rotation of structures in a target introduces additional frequency modulations on the returned signals and also generates sidebands about the center Doppler frequency of the target. In other words, the body image will be contaminated due to the interference from the rotating parts. In this paper, an imaging method for moving targets with rotating parts is presented. The method is simple to implement and is based on the Hough transform (HT), which is widely used in image processing. Using the standard HT and an extended HT, we put forward a separation method by detecting the straight lines and the sinusoids on the spectrogram, respectively. A computer simulation is given to illustrate the effectiveness of the proposed method.