Knowledge-Aided STAP Using Low Rank and Geometry Properties

This paper presents knowledge-aided space-time adaptive processing (KA-STAP) algorithms that exploit the low-rank dominant clutter and the array geometry properties (LRGP) for airborne radar applications. The core idea is to exploit the clutter subspace that is only determined by the space-time steering vectors, by employing the Gram-Schmidt orthogonalization approach to compute the clutter subspace. Simulation results illustrate the effectiveness of our proposed algorithms.

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