Airborne Radar STAP using Sparse Recovery of Clutter Spectrum

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...

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Bibliographic Details
Published in:arXiv.org 2010-08
Main Authors: Sun, Ke, Zhang, Hao, Li, Gang, Meng, Huadong, Wang, Xiqin
Format: Article
Language:English
Subjects:
Airborne radar
Algorithms
Clutter
Covariance matrix
Moving targets
Radar equipment
Recovery
Space-time adaptive processing
Spacetime
Statistical methods
Target detection
Training
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Summary: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.
ISSN:2331-8422