Multi-target simultaneous ISAR imaging based on compressed sensing

Abstract

Conventional range-Doppler (RD) inverse synthetic aperture radar (ISAR) imaging method utilizes coherent integration of consecutive pulses to achieve high cross-range resolution. It requires the radar to keep track of the target during coherent processing intervals (CPI). This restricts the radar's multi-target imaging ability, especially when the targets appear simultaneously in different observing scenes. To solve this problem, this paper proposes a multi-target ISAR imaging method for phased-array radar (PAR) based on compressed sensing (CS). This method explores and exploits the agility of PAR without changing its structure. Firstly, the transmitted pulses are allocated randomly to different targets, and the ISAR image of each target can be then reconstructed from limited echoes using CS algorithm. A pulse allocation scheme is proposed based on the analysis of the target's size and rotation velocity, which can guarantee that every target gets enough pulses for effective CS imaging. Self-adaptive mechanism is utilized to improve the robustness of the pulse allocation method. Simulation results are presented to demonstrate the validity and feasibility of the proposed approach.

From Coarse to Fine: ISAR Object View Interpolation via Flow Estimation and GAN

“…When the traditional Range-Doppler (RD) algorithm is applied for imaging, there are instances when the target's perspective width is inadequate for ISAR imaging, especially when the target is moving in the direction of the radar line-of-sight (RLOS), the rotational components on the Doppler dimension are insufficient for imaging [8], such as shown in Fig. 2 (b); (4) When facing non-cooperative targets with high levels of agility and concealment, there are significant disparities in their occurrence frequencies within complex environments, making it difficult to capture a large number of ISAR target images in real-time, and further leading to incomplete and unbalanced sample acquisition [9]. As a result, there is an urgent and critical need to find a solution for utilizing the limited ISAR data currently available to achieve autonomous data expansion.…”

A New Waveform Design Method for Multi-Target Inverse Synthetic Aperture Radar Imaging Based on Orthogonal Frequency Division Multiplexing Chirp

“…In recent decades, the increase in collaborative attack methods on aerospace targets such as aircraft, missiles and satellites has made the modern battlefield more diverse and complex [1][2][3], which requires the radar to obtain information from multiple targets simultaneously and efficiently [4][5][6][7][8][9][10][11][12][13][14][15][16]. ISAR provides a way to obtain high-resolution images of aerospace targets without limitations imposed by time, weather and environmental factors, playing an important role in space target monitoring, recognition and other fields.…”

Compressive sensing‐based two‐dimensional scattering‐center extraction for incomplete RCS data

“…In addition, the smoothed L0 (SL0) algorithm for CS reconstruction was proposed using a smooth measure of the l 0 -norm by introducing a sequence of smoothed functions to approximate the l 0 -norm [17,18]. While most research works [19][20][21][22][23][24][25] were performed on CS-theory-based radar image reconstruction, the capability of SCE for incomplete RCS datasets with missing data, especially for 2D SCE, has yet to be carefully and sufficiently studied.…”