Abstract
With the rapid development of communication technology in recent years, Wireless Sensor Network (WSN) has become a promising research project. WSN is widely applied in a number of fields such as military, environmental monitoring, space exploration and so on. The non-line-of-sight (NLOS) localization is one of the most essential techniques for WSN. However, the NLOS propagation of WSN is largely influenced by many factors. Hence, a triple filters mixed Kalman Filter (KF) and Unscented Kalman Filter (UKF) voting algorithm based on Fuzzy-C-Means (FCM) and residual analysis (TF-FCM) has been proposed to cope with this problem. Firstly, an NLOS identification algorithm based on residual analysis is used to identify NLOS errors. Then, an NLOS correction algorithm based on voting and NLOS errors classification algorithm based on FCM are used to process the NLOS measurements. Hard NLOS measurements and soft NLOS measurements are classified by FCM classification. Secondly, KF and UKF are applied to filter two categories of NLOS measurements. Thirdly, maximum likelihood localization (ML) is employed to estimate the position of mobile nodes. The simulation result confirms that the accuracy and robustness of TF-FCM are better than IMM, UKF and KF. Finally, an experiment is conducted to test and verify our algorithm which obtains higher localization accuracy.
Highlights
The most popular worldwide localization is the Global Positioning System (GPS) which is used to tackle outdoor positioning problems, but its accuracy cannot meet requirements in some areas especially in indoor environments
This paper proposes a triple filter algorithm based on Kalman Filter (KF) and Unscented Kalman Filter (UKF)
A method of mixed UKF and KF is proposed to filter out measurements noise, and no prior information is required in this mixed algorithm, which can better mitigate NLOS errors
Summary
The most popular worldwide localization is the Global Positioning System (GPS) which is used to tackle outdoor positioning problems, but its accuracy cannot meet requirements in some areas especially in indoor environments. Because of the influence of Non-Line-of-Sight (NLOS) propagation, and other factors such as insufficient position signal strength, electromagnetic interference noise and mobile beacon node, the accuracy of localization has become a critical research direction. In order to tackle the fluctuation problem of range information, many researchers have proposed various filtering algorithms: Average Filtering algorithm (AF), Gauss Filter (GF), Kalman Filter (KF), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filtering (PF) and so on. This paper proposes a triple filter algorithm based on KF and UKF. A method of mixed UKF and KF is proposed to filter out measurements noise, and no prior information is required in this mixed algorithm, which can better mitigate NLOS errors. The NLOS correction algorithm based on voting is proposed to restrict NLOS errors and correct the NLOS measurements, which are used to preprocess the NLOS measurements that need to be classified.
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