NICL: Non-Line-of-Sight Identification in Global Navigation Satellite Systems With Continual Learning

The widespread adoption of Global Navigation Satellite Systems (GNSS) has revolutionized high-precision positioning services. However, the accuracy of these systems is significantly compromised by Non-Line-of-Sight (NLOS) signals, especially in urban canyons. Specifically, Line-of-Sight (LOS) signals are obstructed, and the reflections lead to substantial errors. Machine learning has emerged as a promising tool for identifying NLOS signals. However, traditional methods often train models on extensive datasets from various environments and fine-tune them with new data to adjust to new scenarios. This fine-tuning can result in catastrophic forgetting, which impairs the models' generalization performance across different environments. To address the limitations of model fine-tuning, we introduce NICL, an approach for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u>LOS <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</u>dentification with <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u>ontinual <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u>earning. NICL preserves performance on previously encountered scenarios while adapting to new environments by employing simple regularization techniques that do not require access to the data from the old scenarios. Specifically, our method incorporates Kullback-Leibler divergence regularization and explainability-based regularization to maintain the model's prior outputs and the logic behind those outputs, thus mitigating catastrophic forgetting. Experimental results on real-world GNSS datasets have shown that our NICL method outperforms existing baseline methods, achieving an absolute improvement of 5%-12% in NLOS identification accuracy.