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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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