A Modified Min-Max Method With Adaptive Distance Adjustment for RSSI-Based Indoor Localization

In range-based localization systems based on received signal strength indicator (RSSI), position estimates are determined by measuring RSSI levels between all reference nodes and an unknown target. The RSSI level indicates the distance between the target and references. Because the RSSI signal is time-varying and fluctuates due to multipath effects, particularly in indoor contexts, this variation can cause distance calculation and localization inaccuracies. Inadequate estimation findings can lead to poor judgments throughout the system. In this paper, we present a modified min-max method to reduce RSSI-to-distance error and to improve localization precision. The novelty of this study is that an autonomous reference node identification, area separation, area selection, and adaptive distance adjustment solution are proposed and integrated with the traditional min-max method. The distances between the target and references are automatically measured and compensated. Experiments in real-world scenarios using 2.4 GHz ZigBee/IEEE 802.15.4 wireless networks have been conducted in different indoor environments, including an office room, an electrical machine laboratory, and a second-floor walking corridor. Experimental results show that the proposed method has estimation errors lower than the traditional min-max method: 0.460 m (proposed) and 0.715 m (traditional) for the office room, 0.735 m and 1.503 m for the machine laboratory, and 0.661 m and 1.340 m for the corridor. The proposed method significantly outperforms the min-max method by 35.623%, 51.063%, and 50.627%, respectively. For in mobile target scenarios, the proposed method also provides a more estimated precision of tracking results. Finally, the computational cost analysis of the proposed method in terms of mathematical operations is discussed. The worst-case scenario and the results obtained from the experiments are also reported.