Optimal nonlinear Kalman filter based on local cognitive structure of long-term and short-term memory neural network

Abstract

Abstract: To solve the problems of inaccurate state space model parameters and poor filtering performance in complex nonlinear target tracking, an optimal nonlinear Kalman filter based on local cognitive structure using a long-term and short-term memory neural network is proposed. Firstly, in the framework of Bayesian filtering, long-term and short-term memory neural network is adopted to recognize Kalman gain, which approximates optimal Kalman gain in a data-driven way. It does not need to fully understand the underlying model parameters, and the extended Kalman filter can be performed in the nonlinear state with partial information. Next, an unsupervised off-line training algorithm is used, which does not need to provide real data, but calculates the unsupervised loss function according to internal characteristics of the observation at the next time through filter interpretability. Simulation results show that the proposed filter can significantly improve the performance in comparison with traditional filter when the parameters in nonlinear model are inaccurate.

Key words: extended Kalman filter, long-term and short-term memory neural network, deep learning, data driven

CLC Number:

TP273

CHEN Hui, HU Rong-hai, XI Lei. Optimal nonlinear Kalman filter based on local cognitive structure of long-term and short-term memory neural network[J]. Journal of Lanzhou University of Technology, 2025, 51(4): 88-94.

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