Target Induced Angle Grid Regularized Estimation for Ghost Identification in Automotive Radar

TL;DR

This paper introduces TIGRE, a robust algorithm for identifying ghost targets in automotive radar by leveraging a physics-inspired regularizer to improve angle-grid estimation, especially in low SNR conditions.

Contribution

The paper proposes a novel regularizer integrated into an iterative estimation algorithm, significantly improving ghost target detection in multipath-rich environments.

Findings

Enhanced angle-grid estimation accuracy in low SNR scenarios

Regularizer enforces asymptotic L0 sparsity on the DOA/DOD grid

Numerical experiments show improved ghost target identification

Abstract

This study presents a novel algorithm for identifying ghost targets in automotive radar by estimating complex valued signal strength across a two-dimensional angle grid defined by direction-of-arrival (DOA) and direction-of-departure (DOD). In real-world driving environments, radar signals often undergo multipath propagation due to reflections from surfaces such as guardrails. These indirect paths can produce ghost targets - false detections that appear at incorrect locations - posing challenges to autonomous navigation. A recent method, the Multi-Path Iterative Adaptive Approach (MP-IAA), addresses this by jointly estimating the DOA/DOD angle grid, identifying mismatches as indicators of ghost targets. However, its effectiveness declines in low signal-to-noise ratio (SNR) settings. To enhance robustness, we introduce a physics-inspired regularizer that captures structural patterns inherent to multipath propagation. This regularizer is incorporated into the estimation cost, forming a new loss function that guides our proposed algorithm, TIGRE (Target-Induced angle-Grid Regularized Estimation). TIGRE iteratively minimizes this regularized loss and we show that our proposed regularizer asymptotically enforces L0 sparsity on the DOA/DOD grid. Numerical experiments demonstrate that the proposed method substantially enhances the quality of angle-grid estimation across various multipath scenarios, particularly in low SNR environments, providing a more reliable basis for subsequent ghost target identification.