Fixed-point smoothing estimation algorithm based on information entropy criterion in non-Gaussian environment

For fixed-point smoothing estimation problems in the non-Gaussian environment, this paper proposes a smoothing estimation algorithm based on maximum correntropy as the optimal criterion, which is called fixed-point maximum correntropy smoother (FP-MCS). First, an alternate form of maximum correntropy Kalman filter (MCKF) is given based on matrix transformation. Then, new states are introduced to augment the system, and online iterative equations of the proposed FP-MCS are derived through the new MCKF form. Furthermore, state estimation error covariances are compared before and after smoothing, and performance improvement of the proposed FP-MCS is analyzed theoretically. Meanwhile, its computational complexity is also compared with other algorithms. Finally, an illustrative example is presented to verify the effectiveness and superiority of the proposed FP-MCS in the non-Gaussian mixture noise environment. © 2024 Northeast University. All rights reserved.