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MCC-CKF: A Distance Constrained Kalman Filter Method for Indoor TOA Localization Applications

1,2,*, 1,2, 1,2 and 1,2
1
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2
Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(5), 478; https://doi.org/10.3390/electronics8050478
Received: 22 March 2019 / Revised: 3 April 2019 / Accepted: 3 April 2019 / Published: 29 April 2019
(This article belongs to the Special Issue Emerging Trends in Industrial Communication)
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Abstract

Non-Gaussian noise may have a negative impact on the performance of the Kalman filter (KF), due to its adoption of only second-order statistical information. Thus, KF is not first priority in applications with non-Gaussian noises. The indoor positioning based on arrival of time (TOA) has large errors caused by multipath and non-line of sight (NLOS). This paper introduces the inequality state constraint to enhance the ranging performance. Based on these considerations, we propose a constrained Kalman filter based on the maximum correntropy criterion (MCC-CKF) to enhance the TOA performance in the extreme environment of multipath and non-line of sight. Pratical experimental results indicate that MCC-CKF outperforms other estimators, such as Kalman filter and Kalman filter based on maximum entropy. View Full-Text
Keywords: time of arrival (TOA); indoor localization; nonlin-of-sight (NLOS); non-white Gaussian noise; maximum correntropy criterion (MCC); constrained Kalman filter time of arrival (TOA); indoor localization; nonlin-of-sight (NLOS); non-white Gaussian noise; maximum correntropy criterion (MCC); constrained Kalman filter
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Xu, C.; Ji, M.; Qi, Y.; Zhou, X. MCC-CKF: A Distance Constrained Kalman Filter Method for Indoor TOA Localization Applications. Electronics 2019, 8, 478.

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