Sparsity-Based Space-Time Adaptive Processing for Airborne Radar with Coprime Array and Coprime Pulse Repetition Interval

Xiaoye Wang; Zhaocheng Yang; Jianjun Huang

ICASSP 2018accepted0 citations

Sparsity-Based Space-Time Adaptive Processing for Airborne Radar with Coprime Array and Coprime Pulse Repetition Interval

Xiaoye Wang, Zhaocheng Yang, Jianjun Huang

Abstract

In this paper, we present a sparsity-based space-time adaptive processing (STAP) algorithm with coprime array and coprime pulse repetition interval (PRI). The considered space-time coprime configuration can significantly save the cost. However, the direct STAP does not exploit the advantage of the large aperture brought by coprime configuration and the recently developed spatial-temporal smoothed-based STAP requires a large number of training snapshots. To solve these issues, we propose a sparsity-based STAP algorithm by using the spacial-temporal sparsity of clutter in virtual domain. Simulation results show that the proposed algorithm can obtain a much higher output signal-to-interference-plus-noise ratio and improve the convergence speed.

BibTeX

@inproceedings{icassp2018_sparsitybasedspa,
  title = {Sparsity-Based Space-Time Adaptive Processing for Airborne Radar with Coprime Array and Coprime Pulse Repetition Interval},
  author = {Xiaoye Wang and Zhaocheng Yang and Jianjun Huang},
  booktitle = {ICASSP 2018},
  year = {2018}
}