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Particle filter technology based on parallel genetic resampling

A genetic resampling and particle filter technology, applied in the field of nonlinear filtering algorithm, can solve problems such as premature phenomenon and affecting the application effect of genetic algorithm, so as to improve efficiency, improve comprehensive application performance, and improve the effect of scarcity problem

Inactive Publication Date: 2010-08-18
BEIHANG UNIV
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Problems solved by technology

[0007] In the application process of genetic algorithm, a prominent problem is that it is prone to premature phenomenon, which will seriously affect the application effect of genetic algorithm.

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  • Particle filter technology based on parallel genetic resampling
  • Particle filter technology based on parallel genetic resampling
  • Particle filter technology based on parallel genetic resampling

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Embodiment Construction

[0017] Particle filtering is a process of obtaining the minimum variance estimation of the state by finding a group of random samples propagating in the state space to approximate the probability density function, and replacing the integral operation with the sample mean. Usually the optimal importance distribution may not be able to be analyzed or sampled, so it is necessary to construct the importance distribution. Generally, the bootstrap particle filter uses the state transition distribution as the importance function, but because it does not use the latest observation information, it mainly depends on the system The model does not fully conform to the actual posterior distribution, especially when the observed data appear at the tail of the transition probability or the likelihood function is too concentrated compared with the transition probability, the filter may fail, which is also an important factor that causes particle filter degradation. reason.

[0018] Genetic al...

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Abstract

The invention relates to a particle filter method based on the parallel genetic resampling, which comprises the following steps of: (1) sampling around an initial probability distribution to obtain initial particles, and setting an initial weight; (2) sampling the particles around the state transition probability density by the filter estimation of M particles at the time of k-1 to generate M new particles, wherein M is a natural number; (3) respectively carrying out the weight update on the M particles to obtain the weight of each particle; and (4) optimizing the particle group by using the parallel genetic resampling algorithm. The invention improves the particle filter, inhibits the degradation phenomenon, solves the problem of the particle insufficiency caused by simple random resampling, improves the diversity and the adaptability of the particles, and further improves the performance accuracy for the particle filter.

Description

technical field [0001] The invention relates to the field of nonlinear filter algorithms, in particular to a particle filter method for resampling by using a parallel genetic algorithm. Background technique [0002] The particle filter algorithm is based on the sequential importance sampling (SIS: Sequentiai Importance Sampling) filtering idea of ​​Bayesian sampling estimation. Hammersley et al. proposed the basic SIS method in the late 1950s, and it was further developed in the 1960s. However, because the above-mentioned research has not solved the problems of particle scarcity and computational constraints, it has not attracted people's attention. It was not until the end of the 1980s that the further improvement of computer computing power and a new SIS-based bootstrap (Bootstrap) nonlinear filter method was proposed by Gordon et al. in 1993, which really became the widespread research and practical application of particle filter algorithms. Foundation. [0003] Particl...

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Application Information

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IPC IPC(8): H03H17/00G06N3/12
Inventor 丛丽秦红磊李子昱
Owner BEIHANG UNIV
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