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      A Space-time Adaptive Processing Algorithm Based on Joint Sparse Recovery

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          Abstract

          Sparse recovery Space-Time Adaptive Processing (STAP) methods for obtaining the clutter spectrum require few training samples and can effectively suppress clutter in nonhomogeneous clutter environments. However, presently available sparse recovery STAP methods only use single training samples to recover the clutter spectrum, neglecting information from multiple samples. Moreover, the recovery performance of the abovementioned methods is sensitive to noise. In this study, a subspace-based jointly sparse recovery method is proposed. The information from multiple training samples is fully used and robust clutter suppression performance in noisy environments is achieved. Simulation results show the effectiveness of the proposed method.

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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 April 2014
          : 3
          : 2
          : 229-234
          Affiliations
          [1 ] Air Force Early Warning Academy
          Article
          bac8bb1838064f9cb19f00cd27d044e3
          10.3724/SP.J.1300.2014.13149

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Categories
          Technology (General)
          T1-995

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