Xingyu He

ISAR imaging with sparse stepped frequency waveforms via matrix completion

ABSTRACT Compressive sensing (CS) has been introduced into inverse synthetic aperture radar (ISAR) imaging with sparse stepped frequency waveforms (SSFWs). However, the performance of CS-based method decreases obviously for complex targets imaging in practice due to the CS-induced irregular range cell migration (IRCM) problem in the recovered high-resolution range profiles (HRRPs). In this letter, a novel method based on matrix completion (MC) theory is proposed for ISAR imaging with SSFWs. By reshaping the sparse stepped frequency echo into a Hankel matrix form, the full frequency signal can be recovered via MC algorithms. Thus, HRRPs without IRCM will be produced and an improved ISAR image will be obtained. The simulated results with real data demonstrate the validity of the proposed method.

Moving Target's HRRP Synthesis With Sparse Frequency-Stepped Chirp Signal via Atomic Norm Minimization

Compressive sensing (CS) has been introduced into inverse synthetic aperture radar (ISAR) imaging with partial measurements. However, in the case of transmitting sparse frequency-stepped chirp signal (FSCS), the CS-based method will produce an irregular range cell migration (IRCM) problem in the recovered high-resolution range profiles (HRRPs). The IRCM is induced by the basis mismatch problem in CS, and it will degrade the ISAR image. To obviate the IRCM, an atomic norm minimization (ANM) method is proposed in this letter. By reformulating the ANM as a semidefinite program (SDP), the echo with full FSCS can be recovered using off-the-shelf SDP solvers. Thus, HRRPs without IRCM can be achieved via the conventional inverse fast Fourier transform. As a result, an improved ISAR image will be obtained. Real data results demonstrate the advantages of the proposed method over the CS-based and matrix completion-based methods.

High resolution 3D imaging in MIMO radar with sparse array

High resolution three-dimensional (3D) imaging method using MIMO radar with sparse array is studied in this paper. A method based on compressive sensing (CS) is firstly given. However, the CS-based method has the off-grid problem which will reduce the estimation accuracy of scatterers’ position on the target. Moreover, a high dimensional measurement matrix is required in the CS-based method, which will lead to a heavy storage and computation burden. To solve the two problems of CS, a new method based on matrix completion is proposed in this paper. After reshaping the sparse 3D echo into a low-rank structured matrix, the full 3D echo can be recovered by solving a nuclear norm minimization problem. Then the accurate position of scatterers can be estimated by applying multi-dimensional harmonic retrieval methods to the full 3D echo. Finally, the high resolution 3D image of targets is reconstructed. The effectiveness of the method is validated by the results of comparative simulations.

High-resolution three-dimensional reconstruction for precession targets based on multivariant back-projection

For precession targets, the complex motion and multiple unknown parameters make the three-dimensional (3D) imaging a difficult task. By taking full advantage of the micromotion and signal characteristics of the precession targets, a multivariant complex-valued back-projection method is proposed. This method can achieve the high-resolution 3D imaging and micromotion parameters estimation simultaneously. After the estimation of the precession angle, the 3D coordinates of the scatterers can be obtained and thus the image scaling can be avoided. Experimental results based on simulated data and electromagnetic computation show that the proposed method has an accurate parameter estimation and effective 3D image reconstruction performance.

2D Superresolution ISAR Imaging via Temporally Correlated Multiple Sparse Bayesian Learning

In inverse synthetic aperture radar (ISAR) imaging, the image resolution is always limited by the bandwidth and the observation time. Sparse recovery (SR) is recently proposed to improve the range resolution or cross-range resolution effectively. However, for the two dimensional superresolution case, a SR-induced range cell migration (RCM) occurs among the High-Resolution Range Profiles (HRRPs) and definitely degrades the ISAR image. After that translational motion compensation is completed, the common sparsity of HRRPs is exploited to suppress the RCM in this paper. Furthermore, by taking the temporal correlation of HRRPs into account, an ISAR imaging method based on temporally correlated Multiple Sparse Bayesian Learning is proposed to improve the imaging quality. Simulated data and real data results demonstrate the effectiveness of the proposed method.

Radar pulse completion and high-resolution imaging with SAs based on reweighted ANM

In actual condition, array elements deficiency or transmission errors lead to incomplete data, which is called sparse aperture (SA) data. In inverse synthetic aperture radar (ISAR) imaging, this large-gaped data produces poor-quality ISAR images when using traditional range–Doppler algorithm. Recently, imaging algorithms based on compressed sensing (CS) theory alleviate this problem effectively because CS theory indicates that sparse signal can be reconstructed from incomplete measurements. However, the basis mismatch problem in CS-based algorithms may degrade the ISAR image. In this study, a reweighted atomic-norm minimisation (ANM) (RAM)-based imaging method is proposed. RAM is a gridless sparse method, which can enhance sparsity and resolution. RAM formulates an optimisation problem and iteratively carries out ANM with a sound reweighting strategy. By reformulating the RAM as a semi-definite programme, the echoes with full aperture (FA) are reconstructed from SA data. After that, ISAR imaging with the reconstructed FA data is achieved via the conventional azimuth compression method. Simulated and real data results demonstrate the effectiveness and superiority of the proposed method.

Dynamic ISAR imaging of maneuvering targets based on sparse matrix recovery

For high resolution inverse synthetic aperture radar (ISAR) imaging of maneuvering targets, the Doppler frequency shifts are time varying during the coherent processing interval (CPI). Thus, the conventional range Doppler (RD) ISAR technique does not work properly. By exploiting two-dimensional (2D) sparsity of the target scene, 2D sparse matrix recovery algorithms are applied to achieve super-resolution within a short CPI, during which the Doppler shifts nearly remains constant. Sequential order one negative exponential (SOONE) function is used to measure the sparsity of a 2D signal. A 2D gradient projection (GP) method is developed to solve the SOONE function and thus the 2D-GP-SOONE algorithm is proposed. The algorithm can solve the sparse recovery of 2D signals directly. Then the 2D-GP-SOONE algorithm is used for the dynamic ISAR imaging of maneuvering targets. Theoretical analysis and simulation results show that the proposed method has a lower computational complexity and can achieve the fast recovering of a sparse matrix. Moreover, the proposed method has a better performance in ISAR imaging of maneuvering targets. Exploit 2D sparsity of the target scene, sparse matrix recovery method can be used.We use the SOONE function to measure the sparsity of a 2D signal.A novel 2D-GP-SOONE algorithm is proposed to achieve the sparse matrix recovery.Use the 2D-GP-SOONE algorithm to achieve the ISAR imaging of maneuvering targets.

High-Resolution Imaging and 3-D Reconstruction of Precession Targets by Exploiting Sparse Apertures

Inverse synthetic aperture radar (ISAR) imaging of a precessing target, which is a kind of fast spinning target, is faced with migration through range cell when using traditional imaging algorithms. Theory of compressed sensing (CS) suggests that exact recovery of an unknown sparse signal with an overwhelming probability can be achieved from very limited number of samples. A cycle shift smoothed L0 algorithm based on CS is proposed in this paper for high-resolution ISAR imaging of precessing targets by exploiting sparse apertures. A precessing cone-shaped target model is built and a 3-D reconstruction method based on multistatic ISAR is proposed. Simulations and electromagnetic computation verify the validity of the proposed method.

Automatic recognition of ISAR images based on deep learning

The problem of target classification of non-cooperative airplane using inverse synthetic aperture radar (ISAR) images is studied. ISAR images variation with the radar imaging view makes the classification difficult. The point scatter models of five different airplanes are built. Deep learning methods have been shown to be able to classify images in a variety of disciplines with a high level of accuracy. As an effective deep learning algorithm, sparse autoencoder (SA) learning algorithm can automatically learn features from unlabeled data. In this paper, the SA algorithm is used to extract the features of the ISAR images. The extracted features are served as the input of the softmax classifier and then the recognition and classification of ISAR images are achieved. Simulation results verify the effectiveness and superiority of the proposed method.