Distributed ISAR imaging based on convolution and total variation reweighted l₁ regularization

ABSTRACT

Distributed Inverse Synthetic Aperture Radar (ISAR) systems employ multiple spatially separated radars to observe a target, with the potential to enhance radar imaging azimuth resolution and gather more target information. Due to the existence of gaps in the observation angles of each radars, the coherence between pulses of radar echo is disrupted during fusion imaging, making it challenging for the Range-Doppler (RD) algorithm to achieve well-focused images. This paper proposes an imaging method based on sparse signal recovery (SSR), using the reweighted $l_1$ norm with convolution and total variation as regularization terms. This approach not only yields well-focused ISAR images but also better preserves the overall target information compared to other methods, while also exhibiting excellent noise resistance. In addition, the alternating direction method of multipliers (ADMM) algorithm is utilized for the solution to reduce computational complexity. Simulation and measured data verify the effectiveness of the proposed method.

KEYWORDS:

• Distributed ISAR imaging
• sparse signal recovery
• alternating direction method of multipliers

Acknowledgements

The authors would like to thank the handling editor and the anonymous reviewers for their valuable comments and suggestions for this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported in part by National Natural Science Foundation of China [No. 62371477], Guangdong Science and Technology Program [No. 2019ZT08X751], and Shenzhen Science and Technology Program [No.KQTD20190929172704911].