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Airborne Radar STAP using Sparse Recovery of Clutter Spectrum

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      Abstract

      Space-time adaptive processing (STAP) is an effective tool for detecting a moving target in spaceborne or airborne radar systems. Statistical-based STAP methods generally need sufficient statistically independent and identically distributed (IID) training data to estimate the clutter characteristics. However, most actual clutter scenarios appear only locally stationary and lack sufficient IID training data. In this paper, by exploiting the intrinsic sparsity of the clutter distribution in the angle-Doppler domain, a new STAP algorithm called SR-STAP is proposed, which uses the technique of sparse recovery to estimate the clutter space-time spectrum. Joint sparse recovery with several training samples is also used to improve the estimation performance. Finally, an effective clutter covariance matrix (CCM) estimate and the corresponding STAP filter are designed based on the estimated clutter spectrum. Both the Mountaintop data and simulated experiments have illustrated the fast convergence rate of this approach. Moreover, SR-STAP is less dependent on prior knowledge, so it is more robust to the mismatch in the prior knowledge than knowledge-based STAP methods. Due to these advantages, SR-STAP has great potential for application in actual clutter scenarios.

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      Most cited references 4

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      Rapid Convergence Rate in Adaptive Arrays

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        Space-time adaptive radar performance in heterogeneous clutter

         W.L. Melvin (2000)
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          Space-time adaptive processing: a knowledge-based perspective for airborne radar

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            Author and article information

            Journal
            24 August 2010
            1008.4185

            http://arxiv.org/licenses/nonexclusive-distrib/1.0/

            Custom metadata
            28 pages, 11 figures, Submitted to the IEEE Transactions on Aerospace and Electronic Systems in April,2010
            cs.IT math.IT

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