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| UWB Indoor Positioning Algorithm Based on Kalman Filter and Particle Filter Fusion |
| CHENG Xue-cong1,2,LIU Fu-cai1,2,HUANG Ru-nan1,2 |
1. Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao, Hebei 066004, China
2.Key Lab of Industrial Computer Control of Heibei Province, Yanshan University, Qinhuangdao, Hebei 066004, China |
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Abstract Indoor positioning technology based on ultra-wideband (UWB) has been widely developed.However, the measurement of UWB in LOS (line-of-sight) and NLOS (non-line-of-sight) environments There are different degrees of error in the distance information, so an improved Kalman filter algorithm is proposed to smooth the UWB original data; then a Kalman filter and particle filter (KPF) particle filter and Kalman filter fusion algorithm is proposed.Particle sampling is carried out through the state quantity and error covariance obtained by Kalman filtering, which overcomes the disadvantage that the kinematic model of traditional particle filtering does not match the actual motion, and greatly reduces the phenomenon of particle degradation.After experiments, the positioning accuracy of the algorithm in LOS and NLOS environments is improved by 20.6% and 15.6%, respectively.
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Received: 08 March 2021
Published: 14 October 2022
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2022, Vol. 43 
