Switching Extended Kalman Filter Bank for Indoor Localization Using Wireless Sensor Networks

被引：6
|
作者：
Pak, Jung Min [1 ]
机构：
[1] Wonkwang Univ, Dept Elect Engn, Iksan 54538, South Korea
来源：
ELECTRONICS | 2021年 / 10卷 / 06期
关键词：
extended Kalman filter (EKF); indoor localization; switching extended Kalman filter bank (SEKFB); wireless sensor network (WSN);
D O I：
10.3390/electronics10060718
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a new filtering algorithm, switching extended Kalman filter bank (SEKFB), for indoor localization using wireless sensor networks. SEKFB overcomes the problem of uncertain process-noise covariance that arises when using the constant-velocity motion model for indoor localization. In the SEKFB algorithm, several extended Kalman filters (EKFs) run in parallel using a set of covariance hypotheses, and the most probable output obtained from the EKFs is selected using Mahalanobis distance evaluation. Simulations demonstrated that the SEKFB can provide accurate and reliable localization without the careful selection of process-noise covariance.
引用
收藏
页码：1 / 10
页数：10
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