A Mobile Localization Method in Smart Indoor Environment Using Polynomial Fitting for Wireless Sensor Network

Yan Wang,Yang Yan,Long Cheng,Zhengjian Li

doi:10.1155/2020/6787252

Abstract

The main factor affecting the localization accuracy is nonline of sight (NLOS) error which is caused by the complicated indoor environment such as obstacles and walls. To obviously alleviate NLOS effects, a polynomial fitting-based adjusted Kalman filter (PF-AKF) method in a wireless sensor network (WSN) framework is proposed in this paper. The method employs polynomial fitting to accomplish both NLOS identification and distance prediction. Rather than employing standard deviation of all historical data as NLOS detection threshold, the proposed method identifies NLOS via deviation between fitted curve and measurements. Then, it processes the measurements with adjusted Kalman filter (AKF), conducting weighting filter in the case of NLOS condition. Simulations compare the proposed method with Kalman filter (KF), adjusted Kalman filter (AKF), and Kalman-based interacting multiple model (K-IMM) algorithms, and the results demonstrate the superior performance of the proposed method. Moreover, experimental results obtained from a real indoor environment validate the simulation results.

Highlights

A wireless sensor network (WSN) is a distributed sensor network which consists of numerous tiny sensor nodes
From the distance measurement models, a state vector can be obtained according to the BSm which is defined by θmðkÞ = 1⁄2 domðkÞ vomðkÞ T for k = 1, 2, ⋯, K, where vomðkÞ is the velocity of the MS according to the BSm
The proposed method shows superior performance compared to Kalman filter (KF), adjusted Kalman filter (AKF), and Kalman-based interacting multiple model (K-interacting multiple model (IMM))

Summary

Introduction

A wireless sensor network (WSN) is a distributed sensor network which consists of numerous tiny sensor nodes. It can be used to estimate the position of a mobile target combining distance measurements to multiple nodes of the WSN. Nodes in a WSN localization context are mainly classified as beacon nodes (the location is known) and unknown nodes (the location is unknown). The main task of WSN localization is to calculate the coordinates of the unknown node via biased measurements obtained from the beacon nodes. Some classic methods of range-based indoor localization technology are time of arrival (TOA) [3, 4], time difference of arrival (TDOA) [5, 6], angle of arrival (AOA) [7], and received signal strength (RSS) [8, 9]. The proposed method in this paper employs TOA in consideration of its low complexity and high accuracy [13]

Methods

Results

Conclusion