Energy-Accuracy Balanced Indoor Positioning Using Tabular Q-Learning and Dynamic Kalman Filter
In Indoor Positioning Systems, continuous high-frequency Wi-Fi fingerprint scanning is a primary cause of battery depletion in resource-constrained IoT mobile nodes. While simply extending the scan interval can reduce energy consumption, it often fails to effectively compensate for the resulting data sparsity and positional uncertainty. This often leads to severe tracking lag and path shrinkage in dynamic environments. To overcome these limitations, we propose a joint optimization framework that combines a tabular Q-learning model utilizing spatiotemporal features with a step-coupled Dynamic Kalman Filter(DKF). The proposed system perceives the user’s dynamic state in real time to determine the optimal sampling interval, while simultaneously adjusting the process noise covariance (Q) of the filter to proactively mitigate tracking errors. Experimental results in a real-world ESP32 testbed demonstrate that our approach achieves an 82.5% reduction in data transmission compared to a high-frequency 30ms baseline, while maintaining a highly accurate Root Mean Square Error (RMSE) of 1.31 m. Consequently, the proposed method successfully approaches a Pareto-optimal tradeoff between energy efficiency and localization accuracy.