Abstract
The major challenges for Ultra-wide Band (UWB) indoor ranging systems are the dense multipath and non-line-of-sight (NLOS) problems of the indoor environment. To precisely estimate the time of arrival (TOA) of the first path (FP) in such a poor environment, a novel approach of entropy-based TOA estimation and support vector machine (SVM) regression-based ranging error mitigation is proposed in this paper. The proposed method can estimate the TOA precisely by measuring the randomness of the received signals and mitigate the ranging error without the recognition of the channel conditions. The entropy is used to measure the randomness of the received signals and the FP can be determined by the decision of the sample which is followed by a great entropy decrease. The SVM regression is employed to perform the ranging-error mitigation by the modeling of the regressor between the characteristics of received signals and the ranging error. The presented numerical simulation results show that the proposed approach achieves significant performance improvements in the CM1 to CM4 channels of the IEEE 802.15.4a standard, as compared to conventional approaches.
Highlights
Navigating in indoor environments beyond the coverage of GPS, locating people or objects in particular areas and locating nodes in wireless sensor networks are applicable in both industry and everyday life [1,2,3]
The arrival time of the first path (FP) can be determined by the decision of the sample which is followed by the great entropy decrease
There are a number of problems in conventional approaches when dealing with ranging and ranging-error mitigation in dense multipath and NLOS environments—which in turn cause large-sized errors in the ranging estimation
Summary
Navigating in indoor environments beyond the coverage of GPS, locating people or objects in particular areas and locating nodes in wireless sensor networks are applicable in both industry and everyday life [1,2,3]. In NLOS mitigation, the goal is to reduce the effect of the ranging errors in NLOS conditions [32,33,34,35] This can be achieved by direct path detection which eliminates the ranging results under NLOS conditions to improve the performance. NLOS mitigation can be combined with NLOS identification by assigning different weights to LOS and NLOS signals [37], but the selection of weights is complicated, so a method which can mitigate the ranging error without recognizing channel conditions is worth consideration. The main body of the paper is organized as follows: In Section 2, a description of TOA-based UWB ranging models, the set of factors that degrade the ranging accuracy and the problem statement are given.
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