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Sparsity-aware space–time adaptive processing algorithms with L1-norm regularisation for airborne radar

Sparsity-aware space–time adaptive processing algorithms with L1-norm regularisation for airborne radar

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  • Author(s): Z. Yang 1 ; R.C. de Lamare 2 ; X. Li 1
    • View affiliations
    • Affiliations: 1: Research Institute of Space Electronics, Electronics Science and Engineering School, National University of Defense Technology, Changsha, People's Republic of China
      2: Communications Research Group, Department of Electronics, University of York, York, UK
  • Source: Volume 6, Issue 5, July 2012, p. 413 – 423
    DOI:  10.1049/iet-spr.2011.0254 , Print ISSN 1751-9675, Online ISSN 1751-9683
© The Institution of Engineering and Technology
Published

This study proposes novel sparsity-aware space–time adaptive processing (SA-STAP) algorithms with L1-norm regularisation for airborne phased-array radar applications. The proposed SA-STAP algorithms suppose that a number of samples of the full-rank STAP datacube are not meaningful for processing and the optimal full-rank STAP filter weight vector is sparse, or nearly sparse. The core idea of the proposed method is imposing a sparse regularisation (L1-norm type) to the minimum variance STAP cost function. Under some reasonable assumptions, the authors firstly propose an L1-based sample matrix inversion to compute the optimal filter weight vector. However, it is impractical because of its matrix inversion, which requires a high computational cost when using a large phased-array antenna. In order to compute the STAP parameters in a cost-effective way, the authors devise low-complexity algorithms based on conjugate gradient techniques. A computational complexity comparison with the existing algorithms and an analysis of the proposed algorithms are conducted. Simulation results with both simulated and the Mountain-Top data demonstrate that fast signal-to-interference-plus-noise-ratio convergence and good performance of the proposed algorithms are achieved.

Inspec keywords: filtering theory; antenna phased arrays; conjugate gradient methods; matrix inversion; phased array radar; space-time adaptive processing; radar interference; airborne radar; radar signal processing; computational complexity

Other keywords: mountain-top data; optimal full-rank STAP filter weight vector; minimum variance STAP cost function; low-complexity algorithms; fast signal-to-interference-plus-noise-ratio convergence; sparsity-aware space-time adaptive processing algorithms; computational complexity; conjugate gradient techniques; L1-based sample matrix inversion; airborne phased-array radar applications; full-rank STAP datacube; L1-norm regularisation; SA-STAP algorithms; large phased-array antenna

Subjects: Filtering methods in signal processing; Interpolation and function approximation (numerical analysis); Radar equipment, systems and applications; Antenna arrays; Linear algebra (numerical analysis); Electromagnetic compatibility and interference

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