Distributed Multi-Kernel Maximum Correntropy State-Constrained Kalman Filter Under Deception Attacks

doi:10.1109/TNSE.2024.3506553

Distributed Multi-Kernel Maximum Correntropy State-Constrained Kalman Filter Under Deception Attacks

2025-01-01

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DOI：10.1109/TNSE.2024.3506553

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In this paper, we investigate the distributed robust state estimation of non-Gaussian systems under unknown deception attacks with the imprecise constraint information. Leveraging the advantage of multi-kernel maximum correntropy criterion (MK-MCC) in non-Gaussian signal processing, a novel maximum-a-posterior like utility function (MAP-LUF) is designed inspired by the traditional 2-norm form cost function, where the inaccurate constraint information is taken into consideration. The direct solution of MAP-LUF gives rise to the centralized MK-MCC based state-constrained Kalman filter (C-MKMCSCKF) through fixed point iteration. Subsequently, the corresponding distributed algorithm is obtained by incorporating the consensus average in the computation of sum terms existing in the C-MKMCSCKF algorithm, which enables local information sharing to approximate the centralized estimation accuracy. Furthermore, we also establish the connection between the proposed centralized algorithm and the Banach theorem through dimension extension, and provide the convergence condition. The effectiveness of our proposed algorithms is validated through comparisons with related works in typical target tracking scenarios over sensor network.

Keywords:

Noise

State estimation

Vectors

Signal processing algorithms

Cost function

Accuracy

Target tracking

Kernel

Kalman filters

Convergence

Deception attacks

distributed Kalman filter

multi-kernel correntropy

robust state estimation

sensor networks

state constraint

Distributed Multi-Kernel Maximum Correntropy State-Constrained Kalman Filter Under Deception Attacks

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