Fast, super‐resolution sparse inverse synthetic aperture radar imaging via continuous compressive sensing

Abstract

Conventional inverse synthetic aperture radar (ISAR) imaging with sparse aperture usually suffers from high side lobes and wide main lobes, which limit the applications of radar super‐resolution imaging, multi‐target resolution, and cognitive reconfiguration. This paper proposes a fast, super‐resolution imaging method employing continuous compressive sensing for sparse‐aperture ISAR. First, the received echo in each range bin is characterised as a linear combination of multiple frequencies shown in a continuous atomic set, established into an atomic norm minimisation (ANM) mode. Second, to improve the resolution and reduce the computational burden significantly, a locally convergent iterative algorithm based on the alternating direction method of multipliers, which iteratively performs ANM with a sound reweighting strategy, is implemented. Then, the low‐rank Toeplitz covariance matrix, which contains the information of the target, is obtained. Subsequently, the Vandermonde decomposition of the Toeplitz covariance matrix is performed to acquire the locations and intensities of the scattering points. Finally, the super‐resolution result is generated by depicting the estimated scatterers in the image. Extensive numerical experiments demonstrate that the proposal is highly effective in recovering the super‐resolution image and shows better performance than state‐of‐the‐art methods.

Two-dimensional gridless super-resolution method for ISAR imaging

“…In the third experiment, we compare the FRWTM with FRANM 23 as a super-resolution-based method, SL0 26 as a CS-based method, and SBL 14 as a Bayesian-based method for Yak-42 at −5 and 0 dB SNRs. As shown in Fig.…”

“…Also in Ref. 23, a super-resolution method called fast reweighted atomic norm minimization (FRANM) is presented by including sparse apertures in the problem.…”

“…Moreover, as opposed to the CS methods, 3 , 15 , 16 we do not need a predefined fine grid, which increases the computational complexity of the algorithm. In comparison to the FRANM, 23 the proposed FRWTM increases the resolution of ISAR images by assuming sparsity of samples in both range and azimuth directions, which leads to better performance at low signal-to-noise ratios (SNRs) with lower mean squared errors (MSEs).…”

Gridless high-resolution sparse ISAR imaging method based on atomic norm minimization with Hankel-Toeplitz model

“…The scattering point of ISAR imaging targets has strong sparse characteristics, which provides natural practicality for sparse recovery methods. From the perspective of traditional sparse recovery methods, ISAR signals are considered sparse under specific known basis vectors called grids [8]. A fully automated ISAR imaging algorithm based on sparse Bayesian learning (SBL) is proposed in [9] to achieve super-resolution ISAR imaging with limited pulses.…”

ISAR Pulses Reconstruction and High-resolution Imaging via Frequency-selective Reweighted Atomic Norm Minimization

“…The ISAR acquires the high range resolution for non-cooperative targets via the transmission and reception of wideband signals [2]. It achieves high cross-range resolution by utilising the angle variation between the radar system and the target to synthesise multi-pulse echo signals within the coherent processing interval (CPI) [3]. However, receiving incomplete data often occurs for modern multifunctional radar systems [4].…”