Power Allocation and Measurement Matrix Design for Block CS-Based Distributed MIMO Radars

TL;DR

This paper introduces two novel methods for optimizing power allocation and measurement matrix design in block CS-based distributed MIMO radars, significantly enhancing target parameter estimation accuracy.

Contribution

It proposes new optimal energy allocation and measurement matrix design techniques based on minimizing block-coherence, improving distributed MIMO radar performance.

Findings

Enhanced target parameter estimation accuracy

Improved measurement matrix design reduces coherence

Simulation results validate performance gains

Abstract

Multiple-input multiple-output (MIMO) radars offer higher resolution, better target detection, and more accurate target parameter estimation. Due to the sparsity of the targets in space-velocity domain, we can exploit Compressive Sensing (CS) to improve the performance of MIMO radars when the sampling rate is much less than the Nyquist rate. In distributed MIMO radars, block CS methods can be used instead of classical CS ones for more performance improvement, because the received signal in this group of MIMO radars is a block sparse signal in a basis. In this paper, two new methods are proposed to improve the performance of the block CS-based distributed MIMO radars. The first one is a new method for optimal energy allocation to the transmitters, and the other one is a new method for optimal design of the measurement matrix. These methods are based on the minimization of an upper bound of the sensing matrix block-coherence. Simulation results show an increase in the accuracy of multiple targets parameters estimation for both proposed methods.