Recognition of Blocking Categories for UWB Positioning in Complex Indoor Environment

Abstract

The recognition of non-line-of-sight (NLOS) state is a prerequisite for alleviating NLOS errors and is crucial to ensure the accuracy of positioning. Recent studies only identify the line-of-sight (LOS) state and the NLOS state, but ignore the contribution of occlusion categories to spatial information perception. This paper proposes a bidirectional search algorithm based on maximum correlation, minimum redundancy, and minimum computational cost (BS-mRMRMC). The optimal channel impulse response (CIR) feature set, which can identify NLOS and LOS states well, as well as the blocking categories, are determined by setting the constraint thresholds of both the maximum evaluation index, and the computational cost. The identification of blocking categories provides more effective information for the indoor space perception of ultra-wide band (UWB). Based on the vector projection method, the hierarchical structure of decision tree support vector machine (DT-SVM) is designed to verify the recognition accuracy of each category. Experiments show that the proposed algorithm has an average recognition accuracy of 96.7% for each occlusion category, which is better than those of the other three algorithms based on the same number of CIR signal characteristics of UWB.

An Improved UWB/IMU Tightly Coupled Positioning Algorithm Study

“…Sheng Kunpeng et al [29] proposed a modelling method that takes into account the angle of incidence of the UWB pulse signal to address the problem of localization bias in the non-visual range environment of ultra-wideband systems. Kong et al [30] proposed a bi-directional search algorithm based on maximum correlation, minimum redundancy and minimum computational cost for the identification of NLOS states, which can effectively identify NLOS and LOS states by setting constraint thresholds for maximum evaluation metrics and computational cost, as well as the best channel impulse response (CIR) feature set for the blocking category. Ansaripour et al [31] proposed a real-time attitude estimation system called ViPER+ in order to eliminate the effect of NLOS signals, which overcomes the NLOS situation and accurately determines the boundaries of heavy construction equipment by multiple UWB tags attached to the equipment surface, introducing prior to localization an input correction method to correct the inputs to the localization algorithm.…”

Research on None-Line-of-Sight/Line-of-Sight Identification Method Based on Convolutional Neural Network-Channel Attention Module

“…It is considered to be one of the main challenges faced by high-precision positioning systems. Therefore, NLOS and Line-of-Sight (LOS) identification before positioning is critical [5].…”