An Improved Kalman Filter for TOA Localization using Maximum Correntropy Criterion

Yue Qi, Mengmeng Ji, Cheng Xu, Jiawang Wan, Jie He

Abstract: Kalman filter (KF) has been widely used in data filtering and smoothing applications. However, it is generally limited by the requirement of white-Gaussian noise. In practical applications, such as indoor localization, TOA distance ranging results are mostly affected by multipath and non-line-of-sight (NLOS) factors, which introduces colored Gaussian noise into the system. In this study, we intend to take advantage of maximum correntropy criterion to enhance the ranging performance. We proposed a Constrained Kalman Filter based on the Maximum Correntropy Criterion (MCC-CKF) to realize high-accuracy TOA ranging in the extreme environments of multipath and NLOS.