Hard Sample Meta-Learning for CIR NLOS Identification in UWB Positioning

Abstract

Nonline-of-sight (NLOS) identification is the key technique to improve the accuracy of the channel impulse response (CIR)-based ultrawideband (UWB) positioning system. However, most existing NLOS identification approaches are tailored to static environments and often encounter difficulties in dynamic settings with both temporal and spatial variations, particularly when dealing with limited and hard samples. This article introduces a hard sample meta-learning (HSML) approach to address the issues of NLOS identification across different scenarios and domains. HSML includes two phases: 1) a hard sample meta-training phase and 2) a fine-grained meta-testing phase. During the meta-training phase, we train a two-loop learning network using CIR from multiple scenarios (tasks). The inner loop focuses on learning task-specific features, while the outer loop captures cross-task generalization properties using a cross-entropy loss. Hard samples are identified based on estimated residuals for each task, and a new dataset is created, consisting of both hard samples and samples with small residuals. To improve the robustness against hard samples, we implement a residual-corrected focal loss, which is used to retrain the network on this new dataset. In the fine-grained meta-testing phase, we apply a filtering mechanism based on the tendency of estimated residuals during fine-tuning. This mitigates the risk of poor performance caused by anomalous samples. We validate the effectiveness and robustness of the proposed HSML method using two datasets containing multiple real-world scenarios. Our experimental results demonstrate that HSML outperforms existing models in terms of identification accuracy, robustness and generalization performance.