Moving Target Parameters Estimation in Non-Coherent MIMO Radar Systems

TL;DR

This paper introduces a non-coherent MIMO radar technique for estimating moving target parameters using phase information, employing a maximum likelihood approach that achieves the theoretical performance bound.

Contribution

A novel non-coherent ML estimation method for moving target parameters in MIMO radar, modeling target motion as a polynomial and deriving the CRB for performance benchmarking.

Findings

The proposed ML estimator effectively estimates target motion coefficients.

Simulation results show the estimator achieves the Cramér-Rao Bound.

The method does not require initial phase synchronization of receive antennas.

Abstract

The problem of estimating the parameters of a moving target in multiple-input multiple-output (MIMO) radar is considered and a new approach for estimating the moving target parameters by making use of the phase information associated with each transmit-receive path is introduced. It is required for this technique that different receive antennas have the same time reference, but no synchronization of initial phases of the receive antennas is needed and, therefore, the estimation process is non-coherent. We model the target motion within a certain processing interval as a polynomial of general order. The first three coefficients of such a polynomial correspond to the initial location, velocity, and acceleration of the target, respectively. A new maximum likelihood (ML) technique for estimating the target motion coefficients is developed. It is shown that the considered ML problem can be interpreted as the classic "overdetermined" nonlinear least-squares problem. The proposed ML estimator requires multi-dimensional search over the unknown polynomial coefficients. The Cram\'er-Rao Bound (CRB) for the proposed parameter estimation problem is derived. The performance of the proposed estimator is validated by simulation results and is shown to achieve the CRB.