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Implementation method of metropolis-hastings mutation particle swarm resampling particle filter

A particle filter and implementation method technology, applied in impedance networks, adaptive networks, electrical components, etc., can solve the problems of increasing the risk of divergence, not considering the state probability density distribution, etc.

Inactive Publication Date: 2016-01-13
PLA UNIV OF SCI & TECH
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Problems solved by technology

However, the research of Kennedy.J (1995) shows that the standard particle swarm optimization algorithm has premature convergence phenomenon, and it is easy to fall into the local optimal solution
In response to this problem, Lu Zhensu (2004), Wang Haifeng (2009), and Chen Jianchao (2009) proposed the improvement of ordinary mutation particle swarm optimization, which enhanced the ability of particle swarm to jump out of the local optimal solution, but they did not consider The probability density distribution of the state, which in turn increases the risk of divergence after mutation

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  • Implementation method of metropolis-hastings mutation particle swarm resampling particle filter

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Embodiment Construction

[0035] The specific implementation process of the present invention will be described in detail below, and an example will be given in conjunction with a nonlinear system model.

[0036] The execution steps of MH variation particle swarm optimization resampling particle filter of the present invention are as follows:

[0037] Step 1: Initialize the particle collection Among them, 1≤i≤N, represents the i-th particle in the particle set, and N is the total number of particles; and the initial position of the particle is randomly set and initial velocity Among them, 1≤k≤K, represents the kth sampling point, and K is the total number of sampling points of the signal;

[0038] Step 2: According to the state transition function F of the system k (·), to predict the state of the particle, namely

[0039] Step 3: Calculate the likelihood distribution value of the particles using the observation equation of the system: where Y k is the observed value, Y pred Observations p...

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Abstract

The invention relates to an implementation method of a particle swarm resampling particle filter based on Metropolis-Hastings variation. The invention provides the implementation method of the particle swarm resampling particle filter based on the Metropolis-Hastings variation so as to solve the problem that estimated accuracy of a particle filter is not high when the number of particles is small. The implementation method enables Metropolis-Hastings (MH) movement to be used as a variation operator of particle swarm optimization, an MH variation rule is combined with a speed-position search process of a particle swarm, a resampled particle swarm is more approximate to a real posterior probability density distribution, the problem that a common variation particle swarm algorithm diverges easily is effectively solved, a convergence speed of the particle filter in a sequential estimation process is accelerated, and estimation accuracy of the particle filter is improved. Proved by a simulation test, the particle swarm optimization particle filter based on the Metropolis-Hastings variation can effectively overcome the phenomenon of particle depletion and improve tracking and estimating effects of a nonlinear system.

Description

technical field [0001] The invention belongs to the field of digital signal processing, more specifically relates to the field of nonlinear filtering, and provides a particle filter resampling and a method for realizing the particle filter. Background technique [0002] Particle filters are widely used in non-Gaussian and nonlinear system state estimation, especially in navigation guidance, target tracking, financial analysis, artificial intelligence, blind signal processing and other fields. However, an unavoidable problem in the particle filter is the phenomenon of particle degeneration (Particle Degeneracy), that is, after several iterations of the particle set, except for a few particles, most of the particles have only tiny weights (a small particle weight means that the The contribution of the posterior probability density is small), and the particles with small weights also need to participate in subsequent iterative calculations, increasing the amount of useless calc...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H03H21/00
Inventor 路威张邦宁张杭陈乾陆溪平
Owner PLA UNIV OF SCI & TECH
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