Self-supervised resource-efficient indoor tracking with adaptive anchor scheduling

Abstract

Wireless indoor localization and tracking systems often implicitly assume that all anchors are continuously available. However, this assumption is often impractical under bandwidth and energy constraints. With limited wireless resources, tracking systems must balance localization accuracy with sensing and communication overhead. Therefore, anchor scheduling is crucial as it enables the system to activate only the most informative anchors for a better accuracy-resource tradeoff. This paper proposes a self-supervised framework for joint tracking and anchor scheduling by first utilizing a physics-guided deep state space model to infer target positions and uncertainty without ground truth trajectories. Building on the learned latent representation, we further design an actor-critic-based reinforcement learning scheduler that proactively selects a resource-budgeted set of informative anchors for future ranging. Experiments on real-world ultra-wideband datasets demonstrate that the proposed method achieves a mean absolute localization error of 0.458 m, outperforming classical filters and learning-based baselines. By activating only 3 of the 8 anchors, the proposed method reduces the number of ranging requests by 62.5%. Complementary device-level measurements further show that this scheduling strategy reduces communication latency from 449.93 to 196.40 ms, corresponding to a 56.35% reduction.