Direct Data Domain STAP using Sparse Representation of Clutter Spectrum

TL;DR

This paper introduces a sparse representation-based direct data domain (D3SR) approach for space-time adaptive processing in airborne radar, effectively estimating high-resolution clutter spectra using only test data, improving detection in non-stationary scenarios.

Contribution

The novel D3SR method exploits spectral sparsity to estimate clutter spectra with full system degrees of freedom, outperforming existing D3 techniques in non-stationary clutter environments.

Findings

D3SR achieves higher output signal-clutter-ratio (SCR).

D3SR improves minimum detectable velocity (MDV).

Simulation results confirm D3SR's effectiveness in non-stationary clutter scenarios.

Abstract

Space-time adaptive processing (STAP) is an effective tool for detecting a moving target in the airborne radar system. Due to the fast-changing clutter scenario and/or non side-looking configuration, the stationarity of the training data is destroyed such that the statistical-based methods suffer performance degradation. 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 with only 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 norm minimization. Then the clutter covariance matrix (CCM) and the corresponding adaptive filter can be effectively obtained. Since D3SR maintains the full system DOF, 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). Thus D3SR is more effective against the range-dependent clutter and interference in the non-stationary clutter scenario.