A Hierarchical Voting Based Mixed Filter Localization Method for Wireless Sensor Network in Mixed LOS/NLOS Environments

Abstract

In recent years, the rapid development of microelectronics, wireless communications, and electro-mechanical systems has occurred. The wireless sensor network (WSN) has been widely used in many applications. The localization of a mobile node is one of the key technologies for WSN. Among the factors that would affect the accuracy of mobile localization, non-line of sight (NLOS) propagation caused by a complicated environment plays a vital role. In this paper, we present a hierarchical voting based mixed filter (HVMF) localization method for a mobile node in a mixed line of sight (LOS) and NLOS environment. We firstly propose a condition detection and distance correction algorithm based on hierarchical voting. Then, a mixed square root unscented Kalman filter (SRUKF) and a particle filter (PF) are used to filter the larger measurement error. Finally, the filtered results are subjected to convex optimization and the maximum likelihood estimation to estimate the position of the mobile node. The proposed method does not require prior information about the statistical properties of the NLOS errors and operates in a 2D scenario. It can be applied to time of arrival (TOA), time difference of arrival (TDOA), received signal (RSS), and other measurement methods. The simulation results show that the HVMF algorithm can efficiently reduce the effect of NLOS errors and can achieve higher localization accuracy than the Kalman filter and PF. The proposed algorithm is robust to the NLOS errors.

Neural Network-Based Alzheimer’s Patient Localization for Wireless Sensor Network in an Indoor Environment

“…The authors would like to thank the Department of Computer Engineering Techniques, Electrical Engineering Technical College, Middle Technical University for their support during this study. [46]…”

MS Location Estimation Based on the Artificial Bee Colony Algorithm

“…With the rapid development of deep learning, the performances of target positioning can be further improved by employing iterating calculations to reduce the NLOS errors [ 29 , 30 , 31 , 32 ]. In [ 29 ], hierarchical voting, known as the policy-based algorithm, was conducted before the measured signals enter the conventional filters. The authors of [ 30 ] processed the database of AOA signals with deep convolutional neural (DCN) networks.…”

A Fuzzy C-Means and Hierarchical Voting Based RSSI Quantify Localization Method for Wireless Sensor Network

“…In [26], based on the RSSI measurement of the WiFi signal, two machine learning-based algorithms are proposed to obtain several statistical features of the RSSI time series, and then the hypothesis-based algorithm is used to identify the NLOS measurement. Our previous paper [27] can only be based on the measured distance. This paper can directly use the RSSI measurement.…”