Variational Signal Separation for Automotive Radar Interference Mitigation

TL;DR

This paper presents a variational Bayesian signal separation method for automotive FMCW radar that jointly detects, estimates object parameters, and cancels interference, improving accuracy and robustness under multipath conditions.

Contribution

It introduces a novel variational EM algorithm based on hierarchical Bayesian modeling for interference mitigation in automotive radar systems.

Findings

Achieves near CRLB in object parameter estimation

Effectively cancels interference while estimating object parameters

Demonstrates robustness in multipath interference scenarios

Abstract

Algorithms for mutual interference mitigation and object parameter estimation are a key enabler for automotive applications of frequency-modulated continuous wave (FMCW) radar. In this paper, we introduce a signal separation method to detect and estimate radar object parameters while jointly estimating and successively canceling the interference signal. The underlying signal model poses a challenge, since both the coherent radar echo and the non-coherent interference influenced by individual multipath propagation channels must be considered. Under certain assumptions, the model is described as a superposition of multipath channels weighted by parametric interference chirp envelopes. Inspired by sparse Bayesian learning (SBL), we employ an augmented probabilistic model that uses a hierarchical Gamma-Gaussian prior model for each multipath channel. Based on this, an iterative inference algorithm is derived using the variational expectation-maximization (EM) methodology. The algorithm is statistically evaluated in terms of object parameter estimation accuracy and robustness, indicating that it is fundamentally capable of achieving the Cramer-Rao lower bound (CRLB) with respect to the accuracy of object estimates and it closely follows the radar performance achieved when no interference is present.