Fast, Efficient, and Viable Compressed Sensing, Low-Rank, and Robust Principle Component Analysis Algorithms for Radar Signal Processing

Abstract

Modern radar signal processing techniques make strong use of compressed sensing, affine rank minimization, and robust principle component analysis. The corresponding reconstruction algorithms should fulfill the following desired properties: complex valued, viable in the sense of not requiring parameters that are unknown in practice, fast convergence, low computational complexity, and high reconstruction performance. Although a plethora of reconstruction algorithms are available in the literature, these generally do not meet all of the aforementioned desired properties together. In this paper, a set of algorithms fulfilling these conditions is presented. The desired requirements are met by a combination of turbo-message-passing algorithms and smoothed ℓ0-refinements. Their performance is evaluated by use of extensive numerical simulations and compared with popular conventional algorithms.

Iterative Signal Detection and Channel Estimation with Superimposed Training Sequences for Underwater Acoustic Information Transmission in Time-Varying Environments

“…From the table, we can see that when the correlation coefficient is low, using a short sub-block strategy has better performance. When the correlation coefficient approaches 1, the system basically degenerates into a turbo equalization system [35]. As mentioned in Section 3, the correlation coefficient was estimated from ĥ.…”