ArticleSensors (Basel, Switzerland)2019
A Triple-Filter NLOS Localization Algorithm Based on Fuzzy C-means for Wireless Sensor Networks.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- An Indoor Robust Localization Algorithm Based on Data Association Technique.Sensors (Basel, Switzerland) · 2020Article
- A Grey Model and Mixture Gaussian Residual Analysis-Based Position Estimator in an Indoor Environment.Sensors (Basel, Switzerland) · 2020Article
- Special Issue on Intelligent Systems in Sensor Networks and Internet of Things.Sensors (Basel, Switzerland) · 2020Article
- Cooperative Traffic Signal Control with Traffic Flow Prediction in Multi-Intersection.Sensors (Basel, Switzerland) · 2019Article
- A Localization and Tracking Approach in NLOS Environment Based on Distance and Angle Probability Model.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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.
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