Novel Positioning Scheme Based on Supervised Deep Reinforcement Learning for Indoor Wireless Localization

In this paper, a supervised deep reinforcement learning (SDRL)-based positioning scheme is proposed for indoor wireless localization. The proposed scheme formulates the positioning problem as a Markov decision process and introduces a target-aware reward design based on the artificial potential field (APF) to alleviate the sparse reward problem commonly encountered in search-based reinforcement learning. In the proposed scheme, supervision is provided at the reward level by incorporating the target position into the reward design, rather than at the action level via expert demonstrations. A multi-scale action set with 49 candidates is further adopted to provide a favorable trade-off between estimation accuracy and search efficiency. An anchor-based environment construction strategy is developed by selecting the four strongest reference points (RPs) and transforming their coordinates with respect to the strongest RP. Simulation results show that the proposed scheme achieves a mean absolute error (MAE) below 0.8 m and success rates above 99.1% within 1 m and 99.2% within 2 m under the default Bluetooth Low Energy setting, while the convex-valid rate of the anchor-based environment exceeds 99.5%. Compared with existing methods, the proposed scheme reduces the MAE by approximately 92.3%. Ablation studies confirm that multi-scale actions reduce the average search steps by approximately 69.5% compared with a single-scale baseline. The proposed scheme also retains stable performance across BLE, Wi-Fi, and Zigbee infrastructures when trained under a representative path-loss setting without retraining and maintains sub-meter accuracy under mild shadow fading. These results confirm that the proposed scheme can improve positioning accuracy and search efficiency for indoor wireless localization.