A Novel Sparse Bayesian Space-Time Adaptive Processing Algorithm to Mitigate Off-Grid Effects

Space-time Adaptive Processing Based on Weighted Regularized Sparse Recovery

In this paper, novel space-time adaptive processing algorithms based on sparse recovery (SR-STAP) that utilize weighted l1-norm penalty are proposed to further enforce the sparsity and approximate the original l0-norm. Because the amplitudes of the clutter components from different snapshots are random variables, we design the corresponding weights according to two different ways, i.e., the Cap...

Space-time adaptive processing

Space-Time Adaptive Processing: Fundamentals

In this lecture, we present the principles of space-time adaptive processing (STAP) for radar, applied to moving target indication. We discuss the properties of optimum STAP, as well as problems associated with estimating the adaptive weights not encountered with spatial-only processing (i.e. beamforming).

Space-Time Adaptive Processing: Algorithms

In this lecture, we present some suboptimum STAP algorithms for radar, applied to moving target indication. In addition, we show some MTI results obtained with the multi-channel airborne experimental radar AER-II of FGAN-FHR.

Radar Space-Time Adaptive Processing

Space-time adaptive processing (STAP) is a signal processing technique that was originally developed for detecting slow-moving targets using airborne radars. The general principle of STAP is as follows. The radar transmits a train of M coherent pulses. The echoes from potential targets (and clutter) are collected at each of the N elements of an antenna array. Separate receiver chains are attach...