MCRB on DOA Estimation for Automotive MIMO Radar in the Presence of Multipath

TL;DR

This paper derives a misspecified Cramér-Rao bound (MCRB) for DOA estimation in automotive MIMO radar affected by multipath, providing a tool to predict estimation performance degradation due to model misspecification.

Contribution

The paper introduces the MCRB specifically for DOA estimation in automotive radar with multipath, linking it to traditional bounds and demonstrating its predictive capability.

Findings

MCRB accurately predicts asymptotic estimator performance.

Multipath causes significant degradation in DOA estimation accuracy.

MCRB can be used for radar system design and performance evaluation.

Abstract

Autonomous driving and advanced active safety features require accurate high-resolution sensing capabilities. Automotive radars are the key component of the vehicle sensing suit. However, when these radars operate in proximity to flat surfaces, such as roads and guardrails, they experience a multipath phenomenon that can degrade the accuracy of the direction-of-arrival (DOA) estimation. Presence of multipath leads to misspecification in the radar data model, resulting in estimation performance degradation, which cannot be reliably predicted by conventional performance bounds. In this paper, the misspecified Cram\'er-Rao bound (MCRB), which accounts for model misspecification, is derived for the problem of DOA estimation in the presence of multipath which is ignored by the estimator. Analytical relations between the MCRB and the Cram\'er-Rao bound are established, and the DOA estimation performance degradation due to multipath is investigated. The results show that the MCRB reliably predicts the asymptotic performance of the misspecified maximum-likelihood estimator and therefore, can serve as an efficient tool for automotive radar performance evaluation and system design.