Registration-based compensation using sparse representation in conformal-array STAP

  • Authors:

  • Ke Sun;Huadong Meng;Fabian D. Lapierre;Xiqin Wang

  • Affiliations:

  • Department of Electronic Engineering, Room 901A, Main building, Tsinghua University, Beijing 100084, China;Department of Electronic Engineering, Room 901A, Main building, Tsinghua University, Beijing 100084, China;Royal Military Academy, Department of Electrical Engineering, Brussels 1000, Belgium;Department of Electronic Engineering, Room 901A, Main building, Tsinghua University, Beijing 100084, China

  • Venue:
  • Signal Processing
  • Year:
  • 2011

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Abstract

Space-time adaptive processing (STAP) is a well-known technique in detecting slow-moving targets in the clutter-spreading environment. When considering the STAP system with conformal radar array (CFA), the training data are range-dependent, which results in poor detection performance of traditional statistical-based algorithms. Current registration-based compensation (RBC) is implemented based on a sub-snapshot spectrum using temporal smoothing. In this case, the estimation accuracy of the configuration parameters and the clutter power distribution is limited. In this paper, the technique of sparse representation is introduced into the spectral estimation, and a new compensation method is proposed, namely RBC with sparse representation (SR-RBC). This method first establishes the relationship between the clutter covariance matrix (CCM) and the clutter spectral distribution. Based on this, it avoids the problem of lacking stationary training data and converts the CCM estimation into the solving of the underdetermined equation only with the test cell. Then sparse representation method, like iterative reweighted least square (IRLS) is used to guide the solution of the underdetermined equation towards the actual clutter distribution. Finally, the transform matrix is designed using the CCM estimation so that the processed training data behaves nearly stationary with the test cell. Because the configuration parameters and the clutter spectral response are obtained with full-snapshot using sparse representation, SR-RBC provides more accurate clutter spectral estimation, and the transformed training data are more stationary so that better signal-clutter-ratio (SCR) improvement is achieved.