用逻辑门解析Wi-Fi定位中的神经网络决策，让黑箱变透明。
Towards Explainable Indoor Localization: Interpreting Neural Network Learning on Wi-Fi Fingerprints Using Logic Gates
- 基于逻辑门构建可解释框架，识别关键信号接入点
- 两年实测显示定位误差降低1.1至2.8倍，模型体积缩小3.4至43.3倍
- 适合需要长期稳定部署的智能建筑与物联网系统
基于深度学习的室内定位虽能精准映射Wi-Fi RSS指纹到物理位置，但多数模型为黑箱，难以理解其决策机制或对环境噪声的响应。本文提出LogNet，一种基于逻辑门的可解释框架，能识别每个参考点（RP）中最具影响力的接入点（AP），揭示环境噪声如何干扰定位判断。该方法使模型故障可追溯、可诊断，支持长期稳定部署。在多个真实建筑平面图上，历时两年的测试表明，LogNet不仅能解释深度学习模型内部行为，还能将定位误差降低1.1–2.8倍，模型尺寸缩小3.4–43.3倍，延迟降低1.5–3.6倍，显著优于现有深度学习模型。
原文摘要 · Abstract (English)
Indoor localization using deep learning (DL) has demonstrated strong accuracy in mapping Wi-Fi RSS fingerprints to physical locations; however, most existing DL frameworks function as black-box models, offering limited insight into how predictions are made or how models respond to real-world noise over time. This lack of interpretability hampers our ability to understand the impact of temporal variations - caused by environmental dynamics - and to adapt models for long-term reliability. To address this, we introduce LogNet, a novel logic gate-based framework designed to interpret and enhance DL-based indoor localization. LogNet enables transparent reasoning by identifying which access points (APs) are most influential for each reference point (RP) and reveals how environmental noise disrupts DL-driven localization decisions. This interpretability allows us to trace and diagnose model failures and adapt DL systems for more stable long-term deployments. Evaluations across multiple real-world building floorplans and over two years of temporal variation show that LogNet not only interprets the internal behavior of DL models but also improves performance-achieving up to 1.1x to 2.8x lower localization error, 3.4x to 43.3x smaller model size, and 1.5x to 3.6x lower latency compared to prior DL-based models.
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