WDMA-UWB Indoor Positioning Through Channel Classification-Based NLOS Mitigation Approach

Abstract

Ultrawideband (UWB) is increasingly popular in location-based services. Waveform division multiple access (WDMA) UWB utilizes orthogonal waveforms to achieve multiple access, enhancing system capacity and simplifying implementation. To address non-line-of-sight (NLOS) ranging errors, we propose a channel classification-based NLOS mitigation approach. Channels are categorized into line-of-sight, soft NLOS, and hard NLOS based on direct path attenuation levels. Our mitigation approach includes channel classification, ranging estimation, and positioning. Experiments were conducted under the IEEE 802.15.4a standard. We employ a squeeze and extraction (SE) convolutional neural network (CNN) for dynamic environment channel classification. We propose an adaptive particle filter (APF) adjusting noise standard deviation based on channel classification outcomes to mitigate NLOS effects in ranging data. Least squares estimation (LSE)-guided quasi-Newton positioning method is combined with an anchor selection algorithm to further improve accuracy. Comparison experiments demonstrate the efficacy of our mitigation approach across diverse environments. In scenarios with a substantial amount of range measurements affected by NLOS, the positioning error has decreased by 75%.