A Robust Detection and Optimization Approach for Delayed Measurements in UWB Particle-Filter-Based Indoor Positioning

Abstract

Ultrawideband (UWB) technology has received considerable attention in indoor positioning because of its high ranging accuracy. However, UWB range measurements can be contaminated by the delayed signals resulting from obstruction and reflection in difficult indoor environments. These signals introduce delays to range measurements and degrade positioning accuracy if they are not resolved properly. In order to mitigate the effects of delayed range measurements on positioning and achieve a high-accuracy position estimation, this paper proposes a robust particle-filter-based indoor positioning algorithm. In the proposed algorithm, an outlier detection method is proposed for delayed measurement identification, and a constrained particle sampling method is proposed to optimize the distribution of the predicted particles. The proposed algorithm is assessed rigorously through testing. The test results show that the proposed algorithm can effectively identify delayed range measurements, mitigate their effects on position estimation, and improve positioning accuracy