Extended holosymmetric multi-cell set membership Kalman hybrid filtering method

Abstract

The invention proposes an extended holosymmetric multi-cell set membership Kalman hybrid filtering method. The optimal filtering calculation of a system state variable is performed for a nonlinear system model, and the Taylor series polynomial expansion is executed for approaching to a nonlinear system function, and a system linear equivalent model is obtained. The method focuses on the high-ordertruncation error processing of linear operation, achieves the holosymmetric multi-cell approaching calculation of Taylor series high-order errors, and performs the holosymmetric multi-cell set membership filtering. The conventional Kalman filtering is also employed for the Gaussian noise of the system state variable, and the method achieves the holosymmetric multi-cell and Kalman hybrid filteringcalculation. The method improves the optimal estimation precision of a state variable parameter of a nonlinear system and the system calculation stability. Compared with a conventional extended Kalman filtering algorithm, the method is better in calculation advantages and calculation efficiency through an SLAM system simulation experiment.

Description

technical field [0001] The present invention relates to the technical field of navigation guidance and control in aviation system information processing science, in particular to an extended fully symmetrical polytopic set member Kalman hybrid filter method, which can be applied to autonomous mobile robot real-time positioning and map construction system (Simultaneous Localization And In the problem of Mapping, SLAM), the optimal filtering calculation of the state parameters of the SLAM system error model is realized. Background technique [0002] Estimation problems are divided into two types: one is based on random noise assumption methods, such as Kalman filtering and extended Kalman filtering algorithm, which require that the statistical properties of the noise are known or some of the properties are known; the other is based on the statistical properties of noise In the case of Unknown But Bounded (UBB), the theory and algorithm of Set-Membership Estimation (SME) are th...

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Problems solved by technology

[0004] Aiming at the technical problem of high computational complexity of existing filtering methods, the present invention proposes an extended fully symmetrical polytope set member Kalman hybrid filtering method, which handles the high-order truncation error processing of the linearization operation and linearizes the Taylor series The high-order term error is approximated by the fully symmetrical polytope set member filter, and the Gaussian noise of the system state variable is still calculated by the traditional Kalman filter, which realizes the optimal filtering of the state parameters of the SLAM system error model and improves the calculation efficiency

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