Open Access

ARTICLE

Maximum Correntropy Criterion-Based UKF for Loosely Coupling INS and UWB in Indoor Localization

Yan Wang^*, You Lu, Yuqing Zhou, Zhijian Zhao

Department of Computer and Communication Engineering, Northeastern University, Qinhuangdao, 066004, China

* Corresponding Author: Yan Wang. Email:

Computer Modeling in Engineering & Sciences 2024, 139(3), 2673-2703. https://doi.org/10.32604/cmes.2023.046743

Received 13 October 2023; Accepted 06 December 2023; Issue published 11 March 2024

Abstract

Indoor positioning is a key technology in today’s intelligent environments, and it plays a crucial role in many application areas. This paper proposed an unscented Kalman filter (UKF) based on the maximum correntropy criterion (MCC) instead of the minimum mean square error criterion (MMSE). This innovative approach is applied to the loose coupling of the Inertial Navigation System (INS) and Ultra-Wideband (UWB). By introducing the maximum correntropy criterion, the MCCUKF algorithm dynamically adjusts the covariance matrices of the system noise and the measurement noise, thus enhancing its adaptability to diverse environmental localization requirements. Particularly in the presence of non-Gaussian noise, especially heavy-tailed noise, the MCCUKF exhibits superior accuracy and robustness compared to the traditional UKF. The method initially generates an estimate of the predicted state and covariance matrix through the unscented transform (UT) and then recharacterizes the measurement information using a nonlinear regression method at the cost of the MCC. Subsequently, the state and covariance matrices of the filter are updated by employing the unscented transformation on the measurement equations. Moreover, to mitigate the influence of non-line-of-sight (NLOS) errors positioning accuracy, this paper proposes a k-medoid clustering algorithm based on bisection k-means (Bikmeans). This algorithm preprocesses the UWB distance measurements to yield a more precise position estimation. Simulation results demonstrate that MCCUKF is robust to the uncertainty of UWB and realizes stable integration of INS and UWB systems.

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Keywords

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