Iterative roubust sparse recoery method based on FOCUSS for space-time adaptive processing

Xiaopeng Yang*, Yuze Sun, Tao Zeng, Teng Long

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

4 Citations (Scopus)

Abstract

Conventional Space-time adaptive processing (STAP) requires large numbers of independent and identically distributed (i.i.d.) training samples to ensure the clutter suppression performance, which is hard to be achieved in nonhomogeneous environment. In order to obtain improved clutter suppression with small training support, an iterative sparse recovery STAP algorithm is proposed in this paper. In the proposed method, the clutter spectrum sparse recovery and the calibration of space-time overcomplete dictionary are implemented iteratively, modified focal underdetermined system solution (FOCUSS) with recursive calculation is used to alleviate the recovery error and reduce the computational cost, meanwhile the mismatch of space-time overcomplete dictionary is calibrated by minimized the cost function. Based on the simulated and the actual data, it is verified that the proposed method can not only converge with much smaller training samples compared with conventional STAP methods, but also provide improved performance compared with existing sparsity-based STAP methods.

Original languageEnglish
Publication statusPublished - 2015
EventIET International Radar Conference 2015 - Hangzhou, China
Duration: 14 Oct 201516 Oct 2015

Conference

ConferenceIET International Radar Conference 2015
Country/TerritoryChina
CityHangzhou
Period14/10/1516/10/15

Keywords

  • FOCUSS
  • Robust
  • STAP
  • Small training support
  • Sparse recovery

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Yang, X., Sun, Y., Zeng, T., & Long, T. (2015). Iterative roubust sparse recoery method based on FOCUSS for space-time adaptive processing. Paper presented at IET International Radar Conference 2015, Hangzhou, China.