Direct data domain STAP using sparse representation of clutter spectrum

Authors:
Ke Sun;Huadong Meng;Yongliang Wang;Xiqin Wang
Affiliations:
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China;Department of Electronic Engineering, Tsinghua University, Beijing 100084, China;Science and Research Department, Wuhan Radar Academy, Wuhan 430019, China;Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Venue:
Signal Processing
Year:
2011

Citing 11
Cited 0

Robust STAP algorithms using prior knowledge for airborne radar applications

Signal Processing
Robust adaptive signal processing methods for heterogeneous radar clutter scenarios

Signal Processing - Special section: New trends and findings in antenna array processing for radar
Performance comparison between statistical-based and direct data domain STAPs

Digital Signal Processing
STAP for airborne radar with cylindrical phased array antennas

Signal Processing
High-resolution radar via compressed sensing

IEEE Transactions on Signal Processing
Corrigendum in "Just relax: Convex programming methods for identifying sparse signals in noise"

IEEE Transactions on Information Theory
A sparse signal reconstruction perspective for source localization with sensor arrays

IEEE Transactions on Signal Processing - Part II
An eigenanalysis interference canceler

IEEE Transactions on Signal Processing
Sparse signal reconstruction from limited data using FOCUSS: are-weighted minimum norm algorithm

IEEE Transactions on Signal Processing
Subset selection in noise based on diversity measure minimization

IEEE Transactions on Signal Processing
Stable recovery of sparse overcomplete representations in the presence of noise

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.08

Visualization

Abstract

In the field of space-time adaptive processing (STAP), direct data domain (D3) methods avoid non-stationary training data and can effectively suppress the clutter within the test cell. However, this benefit comes at the cost of a reduced system degree of freedom (DOF), which results in performance loss. In this paper, by exploiting the intrinsic sparsity of the spectral distribution, a new direct data domain approach using sparse representation (D3SR) is proposed, which seeks to estimate the high-resolution space-time spectrum only with the test cell. The simulation of both side-looking and non-side-looking cases has illustrated the effectiveness of the D3SR spectrum estimation using focal underdetermined system solution (FOCUSS) and L"1 norm minimization. Then the clutter covariance matrix (CCM) and the corresponding adaptive filter can be effectively obtained. D3SR maintains the full system DOF so that it can achieve better performance of output signal-clutter-ratio (SCR) and minimum detectable velocity (MDV) than current D3 methods, e.g., direct data domain least squares (D3LS). Therefore D3SR can deal with the non-stationary clutter scenario more effectively, where both the discrete interference and range-dependent clutter exists.