Particle filtering method based on differential evolution

Abstract

The invention discloses a particle filtering method based on differential evolution and belongs to the field of signal processing and nonlinear filtering. The method is an improvement on particle filtering and is mainly characterized in that significance distribution of particle filtering is generated through unscented Kalman filter so as to make full use of latest observation information; differential iteration optimization processing instead of traditional resampling operation is carried out on sampled particles generated through significance distribution so as to obtain an optimal particle point set. According to the method, the process of simply duplicating or discarding particles through a traditional resampling algorithm is replaced by the process of evolving or optimizing the particle set through a differential evolution algorithm, the problems of particle degeneracy and particle impoverishment during particle filtering are effectively mitigated, the utilization ratio of the particles is improved, and estimation accuracy is higher; the method has good application prospects in the field of signal processing and nonlinear filtering.

Description

technical field [0001] The invention belongs to the field of signal processing and nonlinear filtering, and relates to particle filtering technology, in particular to a particle filtering method based on differential evolution. Background technique [0002] Nonlinear system state estimation problems widely exist in many fields such as target tracking, signal processing, automatic control, artificial intelligence, wireless communication and financial analysis. [0003] The extended Kalman filter (Extented Kalman Filter, EKF) is one of the most classic nonlinear filtering methods. Its basic idea is to estimate the state of the nonlinear system by linearizing the nonlinear system. Due to its large linearization error and the fact that it is difficult to obtain the Jacobian matrix of nonlinear functions in many practical problems, its estimation accuracy and application range are greatly limited. Afterwards, the Unscented Kalman Filter (UKF) that does not need to calculate the ...

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Problems solved by technology

[0007] Resampling is another way to reduce particle degradation. Although the weight of particles after resampling is not zero, traditional resampling copies particles with large weights multiple times and discards particles with small weights. , the effective particle sample is gradually depleted, leading to a new problem, the particle sample impoverishment phenomenon

UPF adopts the traditional resampling method, which obviously cannot avoid the problem of particle depletion

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