31 research outputs found
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 fact that the clutter subspace is only determined by the space-time
steering vectors,
{red}{where the Gram-Schmidt orthogonalization approach is employed to
compute the clutter subspace. Specifically, for a side-looking uniformly spaced
linear array, the} algorithm firstly selects a group of linearly independent
space-time steering vectors using LRGP that can represent the clutter subspace.
By performing the Gram-Schmidt orthogonalization procedure, the orthogonal
bases of the clutter subspace are obtained, followed by two approaches to
compute the STAP filter weights. To overcome the performance degradation caused
by the non-ideal effects, a KA-STAP algorithm that combines the covariance
matrix taper (CMT) is proposed. For practical applications, a reduced-dimension
version of the proposed KA-STAP algorithm is also developed. The simulation
results illustrate the effectiveness of our proposed algorithms, and show that
the proposed algorithms converge rapidly and provide a SINR improvement over
existing methods when using a very small number of snapshots.Comment: 16 figures, 12 pages. IEEE Transactions on Aerospace and Electronic
Systems, 201