51 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
Study of Enhanced MISC-Based Sparse Arrays with High uDOFs and Low Mutual Coupling
In this letter, inspired by the maximum inter-element spacing (IES)
constraint (MISC) criterion, an enhanced MISC-based (EMISC) sparse array (SA)
with high uniform degrees-of-freedom (uDOFs) and low mutual-coupling (MC) is
proposed, analyzed and discussed in detail. For the EMISC SA, an IES set is
first determined by the maximum IES and number of elements. Then, the EMISC SA
is composed of seven uniform linear sub-arrays (ULSAs) derived from an IES set.
An analysis of the uDOFs and weight function shows that, the proposed EMISC SA
outperforms the IMISC SA in terms of uDOF and MC. Simulation results show a
significant advantage of the EMISC SA over other existing SAs.Comment: 6 pages 4 figure
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