Quantum mechanics

Algorithm

Gaussian

Control (management)

Extended Kalman filter

Computer science

Fast Kalman filter

Physics

Kernel (algebra)

Mathematics

Image (mathematics)

Artificial intelligence

Kalman filter

Combinatorics

Invariant extended Kalman filter

Control theory (sociology)

Noise (video)

IEEE Transactions on Circuits & Systems II Express Briefs

Traditional interacting multiple model Kalman filter (IMM-KF) was derived for linear discrete time system with Markovian coefficients and it works well under Gaussian noise. In practice, when the system is affected by the impulsive noise, the performance of the traditional IMM-KF model becomes poor. Then, maximum correntropy Kalman filter (MCKF) which has advantages in dealing with non-Gaussian noise was proposed. However, the performance of traditional MCKF becomes poor when the motion state is not single. In this brief, we propose a new algorithm called interacting multiple model based on maximum correntropy Kalman filter (IMM-MCKF) by combining interacting multiple model (IMM) with MCKF to deal with the impulsive noise. Also, the traditional MCKF works poorly when the kernel bandwidth is small, we made a further improvement by changing the kernel. Simulation examples are given to demonstrate the effectiveness of the new algorithms.

Interacting Multiple Model Based on Maximum Correntropy Kalman Filter