A Novel Compressed Sensing Based Method for Space Time Signal Processing for Airborne Radars

Abstract

Abstract-Space time adaptive processing (STAP) is a signal processing technique for detecting slowly moving targets using airborne radars. The traditional STAP algorithm uses a lot of training cells to estimate the space-time covariance matrix, which occupies large computer memory and is time-consuming. Recently, a number of compressed sensing based STAP algorithms are proposed to detect moving target in strong clutter situation. However, the coherence of the sensing matrix is not low due to the high resolution of the DOA (direction of arrival)-Doppler plane, which does not guarantee a good reconstruction of the sparse vector with large probability. Consequently, the direct estimation of the target amplitude may be unreliable using sparse representation when locating a moving target from the surrounding strong clutter. In this study, a novel method named similar sensing matrix pursuit is proposed to reconstruct the sparse radar scene directly based on the test cell, which reduces the computing complexity efficiently. The proposed method can efficiently cope with the deterministic sensing matrix with high coherence. The proposed method can estimate the weak elements (targets) as well as the prominent elements (clutter) in the DOA-Doppler plane accurately, and distinguish the targets from clutter successfully.

Image Reconstruction From Highly Sparse and Limited Angular Diffraction Tomography Using Compressed Sensing Approach

“…Thus a few projections are sufficient to create an almost distortion free reconstruction. Hence it is of great importance in all applications dealing with highly limited projection data as in medical imaging [12][13][14][15][16][17], data channel sampling [18], radar imaging [19][20][21], spectrum sensing in cognitive radio systems [22] and many other applications. A steady focus is also directed towards improving the data acquisition and reconstruction frameworks for increasing the present capabilities of compressed sensing [23][24][25].…”