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access icon free Thinned knowledge-aided STAP by exploiting structural covariance matrix

© The Institution of Engineering and Technology
Received 17/02/2017, Accepted 11/04/2017, Revised 02/04/2017, Published 13/04/2017

The authors propose a thinned knowledge-aided space–time adaptive processing (STAP) scheme based on structural prior information of the clutter covariance matrix (CCM). Due to the low-rank Toeplitz-block-Toeplitz structure of the CCM, the CCM can be expressed by a series of basis matrices on the clutter ridge. In contrast to the expression based on the Vandermonde decomposition of the CCM, this expression can avoid searching of the clutter subspace. This expression also allows reducing the dimension of STAP and estimating the CCM with compressed data. Based on this expression, the authors derive a closed-form CCM estimate using a modified generalised least squares (GLS) method, and the proposed estimator is unbiased, consistent and more asymptotically efficient than the conventional GLS method. We derive the average signal-to-clutter-noise ratio loss (SCNRL) of the STAP filter using the proposed CCM estimates. Exploiting the prior structural information of the CCM can enhance the STAP performance with a limited sample size, and a lower compression rate can achieve more improvement. Finally, a unified framework is proposed for covariance estimation and SCNRL analysis when structural information of the CCM is exploited. Simulations also validate these results.

Inspec keywords: space-time adaptive processing; covariance matrices; least squares approximations; signal processing; clutter

Other keywords: thinned knowledge aided STAP; exploiting structural covariance matrix; SCNRL; clutter subspace; structural prior information; average signal-to-clutter-noise ratio loss; low-rank Toeplitz-block-Toeplitz structure; clutter covariance matrix; generalised least squares method; covariance estimation; STAP filter; Vandermonde decomposition; CCM; space-time adaptive processing; clutter ridge

Subjects: Electromagnetic compatibility and interference; Interpolation and function approximation (numerical analysis); Algebra; Radar equipment, systems and applications; Signal processing and detection

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