Jia Duan

Sparse Apertures ISAR Imaging and Scaling for Maneuvering Targets

In advanced multifunctional radar, inverse synthetic aperture radar (ISAR) imaging of sparse apertures for maneuvering targets is a challenge problem. In general, the Doppler modulation of rotation motion can be modeled as linear frequency for uniformly accelerated rotation targets, which is spatial-variant in two-dimension (2-D). The signal diversity inherently reflects the maneuverability and provides a rationale of rotation motion estimation. In this paper, we focus on the problem of sparse apertures ISAR imaging and scaling for maneuvering targets. The maneuvering signal model is formulated as chirp code and represented using a chirp-Fourier basis. Then sparse representation is applied to realize range-Doppler (RD) imaging from the sparse apertures, where the superposition of chirp parameters is acquired using the modified discrete chirp Fourier transform (MDCFT). After preprocessing, such as sample selection, rotation center determination, and noise reduction, the chirp parameters are used to estimate the parameters of rotation motion using the weighted least square (WLS) method. Finally, a high-resolution scaled-ISAR image is achieved by rescaling the acquired RD image using the estimated rotation velocity. Experiments are performed to confirm the effectiveness of the proposal.

Phase adjustment and isar imaging of maneuvering targets with sparse apertures

A multifunctional radar system can only acquire limited and discontinuous wideband pulses, which form sparse aperture (SA) observations of a target. To carry out radar activities (detection, tracking, and imaging) simultaneously for multiple targets, inverse synthetic aperture radar (ISAR) imaging exploiting these SA data is essential for multifunctional radar. In this paper, we study the phase adjustment and full-aperture (FA) reconstruction for SA-ISAR imaging of maneuvering targets. A modified eigenvector-based autofocus approach is proposed to correct phase errors within SA measurements of maneuvering targets. After phase correction, the FA data are reconstructed from SA measurements via sparse representation under a redundant chirp-Fourier dictionary. An efficient algorithm is developed to solve the sparse decomposition optimization, and ISAR images of the maneuvering target are obtained by adaptive joint time-frequency imaging approaches with the reconstructed data. Both simulated and real data sets are used to confirm the effectiveness of the proposed methods.

Polarimetric Target Decomposition Based on Attributed Scattering Center Model for Synthetic Aperture Radar Targets

In this letter, a novel polarimetric target decomposition (PTD) method based on the attributed scattering center (ASC) model is proposed for man-made targets in synthetic aperture radar (SAR) images. By extracting attributed parameters, polarimetric characteristics of targets can be exploited by performing PTD on the extracting parameters of ASCs instead of pixels in conventional PTD algorithms. As a result, the integrity of target components is enhanced, leading to a reliable analysis on the polarimetric scattering mechanisms of SAR targets. In the proposal, an attributed parameters extraction method based on joint exploitation of multiple polarimetric channels and a target discriminating method based on a constant-false-alarm threshold are developed to improve its robustness in strong noise scenarios. Experimental results confirm the effectiveness of the proposed algorithm.

Translational motion compensation for ISAR imaging under low SNR by minimum entropy

In general, conventional error correction for inverse synthetic aperture radarimaging consists of range alignment and phase adjustment, which compensate range shift and phase error, respectively. Minimum entropy-based methods have been proposed to realize range alignment and phase adjustment. However, it becomes challenging to align high-resolution profiles when strong noise presents, even using entropy minimization. Consequently, the subsequent phase adjustment fails to correct phase errors. In this article, we propose a novel method for translational motion correction, where entropy minimization is utilized to achieve range alignment and phase adjustment jointly. And, a coordinate descent algorithm is proposed to solve the optimization implemented by quasi-Newton algorithm. Moreover, a method for coarse motion estimation is also proposed for initialization in solving the optimization. Both simulated and real-measured datasets are used to confirm the effectiveness of the joint motion correction in low signal-to-noise ratio situations.

A weighted eigenvector autofocus method for sparse-aperture ISAR imaging

With the development of multi-functional radar systems, inverse synthetic aperture radar (ISAR) imaging with sparse-aperture (SA) data has drawn considerable attention in the recent years. Motion compensation and imaging are among the most significant challenges that SA-ISAR imaging frequently faces. In this paper, we focus on the autofocus scheme, in which a modified eigenvector-based autofocus method is proposed. In the method, different weights are endued to different range cells according to their signal-to-noise ratios (SNRs). Using the weights, the contribution from the range cells with high SNR is enhanced, yielding accuracy improvement in phase error estimation. What is more is that to improve the estimation precision, an iterative scheme is introduced. Experimental results show that the proposal is not only robust to severe noise but also applicable to ISAR imaging with different SA patterns. Detailed comparisons are given in order to show the superiorities of the proposal in phase adjustment for ISAR data.

Super-resolution imaging algorithm based on attributed scattering center model

Based on attributed scattering center, a novel algorithm of super-resolution synthetic aperture radar (SAR) imaging is proposed in this paper to characterize physical properties of the scattering object. An improved Orthogonal Matching Pursuit (OMP) algorithm is utilized to estimate the parameter of the simplified attributed scattering center model. To realize super-resolution, spectrum extrapolation is preformed in signal reconstruction based on the estimated model parameter. The method can accurately describe the physical structure compared with the traditional super-resolution algorithm based on the point scattering model. In addition, the method has fast computation efficiency and can achieve a better focusing performance with the utilization of the orientation angle. The simulation results validate the superiority of the proposed algorithm.

A novel signal reconstructing method for radar targets

In this paper, a novel signal reconstructing method for radar targets is proposed based on the attributed scattering center model. By extracting the attributed parameters, the large amount of target data can be represented by small amounts of attributed parameters. In this way, the data amount has been compressed sharply, which releases the computer memory for storage. After extraction, a target discriminating method is presented by applying a CFAR threshold to the energy of extracted attributed scattering centers, by which, weak distributed scattering centers with relatively high energy in total can be discriminated from noise under low SNRs. Experimental results validate the effectiveness of the signal reconstructing capability of the proposal.