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

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.

💡 Research Summary

The paper addresses the problem of ghost‑target detection in automotive MIMO radar, where multipath reflections from objects such as guardrails generate false detections that appear at incorrect angles. Existing work, notably the Multi‑Path Iterative Adaptive Approach (MP‑IAA), jointly estimates a two‑dimensional angle grid (DOA × DOD) and flags grid cells with mismatched DOA and DOD as ghosts. However, MP‑IAA’s performance deteriorates sharply under low signal‑to‑noise ratio (SNR) conditions, which are common in urban driving scenarios.

To overcome this limitation, the authors propose a new algorithm called TIGRE (Target‑Induced angle‑Grid Regularized Estimation). The core contribution is a physics‑inspired regularizer that encodes the structural property of first‑order multipath: a ghost target shares either the DOA or the DOD of the real target, but not both. Mathematically, for each grid element (X_{g,q}) the regularizer weight is defined as

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