Knowledge-Aided STAP Using Low Rank and Geometry Properties

Abstract EN

This paper presents knowledge-aided space-time adaptive processing (KA-STAP) algorithms thatexploit the low-rank dominant clutter and the array geometry properties (LRGP) for airborne radarapplications.

The core idea is to exploit the clutter subspace that is only determined by the space-timesteering vectors, by employing the Gram-Schmidt orthogonalization approach to compute the cluttersubspace.

Simulation results illustrate the effectiveness of our proposed algorithms.