MIMO radar waveform design based on mutual information and minimum mean-square error estimation

TLDR

Radar systems include conventional single‑transmitter/receiver setups and newer MIMO configurations. The study seeks to design radar waveforms that improve target identification and classification. Using a random target impulse response model, the authors formulate two constrained optimization problems—maximizing mutual information and minimizing mean‑square error—which yield the same optimal waveform matrix via a water‑filling power allocation, and also propose an asymptotic variant requiring less target statistical knowledge. Under a fixed total power budget, both the mutual‑information maximization and mean‑square‑error minimization lead to identical optimal waveform designs.

Abstract

This paper addresses the problem of radar waveform design for target identification and classification. Both the ordinary radar with a single transmitter and receiver and the recently proposed multiple-input multiple-output (MIMO) radar are considered. A random target impulse response is used to model the scattering characteristics of the extended (nonpoint) target, and two radar waveform design problems with constraints on waveform power have been investigated. The first one is to design waveforms that maximize the conditional mutual information (MI) between the random target impulse response and the reflected waveforms given the knowledge of transmitted waveforms. The second one is to find transmitted waveforms that minimize the mean-square error (MSE) in estimating the target impulse response. Our analysis indicates that under the same total power constraint, these two criteria lead to the same solution for a matrix which specifies the essential part of the optimum waveform design. The solution employs water-filling to allocate the limited power appropriately. We also present an asymptotic formulation which requires less knowledge of the statistical model of the target