Energy-Based Timing Estimation and Artificial Neural Network Based Ranging Error Mitigation in mm-Wave Ranging Systems Using Statistics Fingerprint Analysis

Liang, Xiaolin; Gulliver, T. Aaron

doi:10.1007/s11277-019-06629-y

Cited by 1 publication

(1 citation statement)

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“…7 According to the different measurement method, the localization of the WSNs can be divided into received signal strength indicator (RSSI), 8 time difference of arrival (TDOA), 9 angle of arrival (AOA), 10 and time of arrival (TOA). 11,12 The range-based localization algorithm can achieve high localization accuracy with a lot of calculation and communication energy consumption. Therefore, it is not suitable for low-cost and low-power applications.…”

Section: Introductionmentioning

confidence: 99%

A mobile localization method based on a robust extend Kalman filter and improved M-estimation in Internet of things

Lin

Luan

et al. 2020

International Journal of Distributed Sensor Networks

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As the key technology for Internet of things, wireless sensor networks have received more attentions in recent years. Mobile localization is one of the significant topics in wireless sensor networks. In wireless sensor network, non-line-of-sight propagation is a common phenomenon leading to the growing non-line-of-sight error. It is a fatal impact for the localization accuracy of the mobile target. In this article, a novel method based on the nearest neighbor variable estimation is proposed to mitigate the non-line-of-sight error. First, the linear regression model of the extended Kalman filter is used to obtain the residual of the distance measurement value. After that, the residual analysis is used to complete the identification of the measurement value state. Then, by analyzing the statistical characteristics of the non-line-of-sight residual, the nearest neighbor variable estimation is proposed to estimate the probability density function of residual. Finally, the improved M-estimation is proposed to locate the mobile robot. Experiment results prove that the accuracy and robustness of the proposed algorithm are better than other methods in the mixed line-of-sight/non-line-of-sight environment. The proposed algorithm effectively inhibits the non-line-of-sight error.

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