An Adaptive Location-Based Tracking Algorithm Using Wireless Sensor Network for Smart Factory Environment

Po-Chih Chiu,Tsung-Yin Ou,Guan-Yu Lin,Ming-Hung Shu,Wei-Chieh Hsiao,Chih-Lung Yu,Kuo-Wei Su,Chen-Yang Cheng,Kuei-Hu Chang

doi:10.1155/2021/4325708

Abstract

In recent years, how to improve the performance of smart factories and reduce the cost of operation has been the focus of industry attention. This study proposes a new type of location-based service (LBS) to improve the accuracy of location information delivered by self-propelled robots. Traditional localization algorithms based on signal strength cannot produce accurate localization results because of the multipath effect. This study proposes a localization algorithm that combines the Kalman filter (KF) and the adaptive-network-based fuzzy inference system (ANFIS). Specifically, the KF was adopted to eliminate noise during the signal transmission process. Through the learning of the ANFIS, the environment parameter suitable for the target was generated, to overcome the deficiency of traditional localization algorithms that cannot obtain real signal strength. In this study, an experiment was conducted in a real environment to compare the proposed localization algorithm with other commonly used algorithms. The experimental results show that the proposed localization algorithm produces minimal errors and stable localization results.

Highlights

Po-Chih Chiu,1,2 Kuo-Wei Su,3 Tsung-Yin Ou,4 Chih-Lung Yu,2 Chen-Yang Cheng,5 Wei-Chieh Hsiao,6 Ming-Hung Shu,2,7 and Guan-Yu Lin 8
Traditional localization algorithms based on signal strength cannot produce accurate localization results because of the multipath effect. is study proposes a localization algorithm that combines the Kalman filter (KF) and the adaptive-network-based fuzzy inference system (ANFIS)
Considering the use of antennas to receive the signal of the reference tag and the object to be measured, the signal will be distorted by the influence of multipath during the transmission process. erefore, this study uses the characteristics of the Kalman filter (KF) to eliminate the noise during the transmission of the signal, so that the received signal is clean. e KF was integrated with the modified Friis free-space model to identify the relationship between signals and distance. rough the correction learning of the ANFIS, the environment parameter suitable for the target was yielded, which was combined with the modified WCG to determine the location of the target

Summary

Related Work

Among the propagation path loss models, the most widely used models are the free-space propagation, log-distance path loss, and Hata models. When the distance between a transmitter and receiver is given, the model can be used to calculate the average received power of the receiver as follows [14]: Pr(d) P(4tGπ)tG2dr2λL2,. E calculation of antenna gains is related to the effective aperture (Ae):. E calculation of the KF is a regression process that involves two steps, namely, prediction and correction. The system state (x􏽢k−1) at time k-1 is used to calculate the a priori state of the system (x􏽢−k ) at time k. Pk I − KkH􏼁P−k , where P−k is the a priori estimation error of the system at time k, pk is the estimation error of the system at time k, and Kk indicates the Kalman gain of the system at time k. In the KF, the Kalman gain is applied to adjust the state estimates; the estimation errors of the systems can converge over time. No parameters are involved in the layers comprising circles. e layers are explained as follows

Layer 1

Layer 5

Methods

Findings

Conclusion