Label-Free Performance Estimation of Deep Learning Based Radar Detectors in Time-Varying Environments

Abstract

Deep-learning-based radar target detectors can achieve strong performance in complex sensing environments. However, in time-varying environments, changes in clutter characteristics may cause the distribution of echoes to deviate from that of the training data, thereby degrading detector reliability. Timely identification of such degradation requires an accurate assessment of the deployed detector's performance; however, ground-truth labels for radar echoes are typically unavailable during deployment. This paper investigates label-free performance estimation for deployed deep learning-based radar target detectors. We first show that confidence-based estimators tend to overestimate detection performance under distribution shift because they fail to account for the discrepancy between the detector-induced pseudo-label distribution and the true label distribution. To address this limitation, we propose probabilistic optimal transport (POT), which assesses deployment performance by computing the Wasserstein distance between the detector's output probability distribution on unlabeled deployment echoes and the label distribution in the training data. We further develop thresholded probabilistic optimal transport (TPOT), which suppresses the influence of anomalous transport costs on the Wasserstein-distance-based estimate, thereby improving estimation robustness. Experiments on a publicly measured radar dataset demonstrate that POT and TPOT consistently outperform representative label-free baselines across deployment scenarios with varying degrees of data distribution shift.