MIMO Radar Transmit Signal Optimization for Target Localization Exploiting Prior Information

TL;DR

This paper develops an optimized MIMO radar transmit signal design that leverages prior target angle information to improve localization accuracy, providing theoretical bounds and a closed-form solution.

Contribution

It introduces a novel transmit signal optimization method exploiting prior information, deriving a closed-form optimal solution and demonstrating improved performance over benchmarks.

Findings

Optimized signals reduce the PCRB compared to benchmarks.

The optimal covariance matrix is rank-one.

Numerical results confirm significant performance improvements.

Abstract

In this paper, we consider a multiple-input multiple-output (MIMO) radar system for localizing a target based on its reflected echo signals. Specifically, we aim to estimate the random and unknown angle information of the target, by exploiting its prior distribution information. First, we characterize the estimation performance by deriving the posterior Cram\'er-Rao bound (PCRB), which quantifies a lower bound of the estimation mean-squared error (MSE). Since the PCRB is in a complicated form, we derive a tight upper bound of it to approximate the estimation performance. Based on this, we analytically show that by exploiting the prior distribution information, the PCRB is always no larger than the Cram\'er-Rao bound (CRB) averaged over random angle realizations without prior information exploitation. Next, we formulate the transmit signal optimization problem to minimize the PCRB upper bound. We show that the optimal sample covariance matrix has a rank-one structure, and derive the optimal signal solution in closed form. Numerical results show that our proposed design achieves significantly improved PCRB performance compared to various benchmark schemes.