An Improved Kalman Filter for TOA Localization using Maximum Correntropy Criterion

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.