Precision improvement for indoor positioning based on fuzzy inference system with ultra-wideband wireless communications

Abstract

This paper investigates the enhancement of positioning accuracy in indoor non-line-of-sight (NLOS) environments using ultra-wideband (UWB) and angle of arrival (AoA) technologies. It examines the application of moving average filters and the adaptive offset cancellation (AOC) method in known target’s location area scenario. Furthermore, this study evaluates the performance of positioning accuracy using various input membership functions in fuzzy inference systems for aera recognition in unknown target’s location area situation, in conjunction with the AOC method. Experimental results show that the AOC method effectively reduces positioning errors by an average of 29.38 cm across twelve test points when the area where the target is located are known, achieving an error reduction to within 20 cm. In cases where target’s location area is unknown, the fuzzy inference system using fuzzy triple std as input membership function achieves an average regional recognition accuracy of 95.68%, outperforming other methods. The proposed fuzzy inference combined with AOC (FAOC) method improves the average positioning error by 40.9% compared to the original positioning method, reducing from 69.03 cm to 44.48 cm.