Robust and fast iterative sparse recovery method for space-time adaptive processing

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...

Iterative Null-space Projection Method with Adaptive Thresholding in Sparse Signal Recovery and Matrix Completion

Adaptive thresholding methods have proved to yield high Signal-to-Noise Ratio (SNR) and fast convergence in finding the solution to the Compressed Sensing (CS) problems. Recently, It was observed that the robustness of a class of iterative sparse recovery algorithms such as Iterative Method with Adaptive Thresholding (IMAT) outperforms the well-known LASSO algorithm in terms of reconstruction q...

A Modified Fast Approximated Power Iteration Subspace Tracking Method for Space-Time Adaptive Processing

We propose a subspace-tracking-based space-time adaptive processing technique for airborne radar applications. By applying a modified approximated power iteration subspace tracing algorithm, the principal subspace in which the clutter-plus-interference reside is estimated. Therefore, the moving targets are detected by projecting the data on the minor subspace which is orthogonal to the principa...

Robust Space-Time Adaptive Processing Using Projection Statistics

In this paper, projection statistics (PS) are applied to detect and mitigate outliers in adaptive processing algorithms. Outliers present in training data result in slow adaptive processor convergence. In radar, these outliers may arise from clutter discretes, desired targets, or jammers. PS provide a computationally tractable technique for identifying and mitigating outlier data samples prior ...

Space-time adaptive processing