Improved particle filtering method based on niche genetic algorithm

A genetic algorithm and particle filter technology, applied in the field of nonlinear filter algorithm, can solve the problem that the optimization result converges to the local optimum, and achieve the effect of improving the particle shortage problem, improving performance, increasing particle diversity and adaptability

Inactive Publication Date: 2010-05-19
BEIHANG UNIV
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However, in the method of resampling using GA, since its hybridization is completely random, although this randomized hybridization form guarantees the diversity of understanding in the initial stage of optimization, after several generations of evolution, a large

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  • Improved particle filtering method based on niche genetic algorithm
  • Improved particle filtering method based on niche genetic algorithm
  • Improved particle filtering method based on niche genetic algorithm

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[0030] Particle filter adopts Sequential Importance Sampling Algorithm (SIS), which is a continuous Monte Carlo method, which can convert integral operation into summation operation of finite points. The key idea is to use a group of random samples with relevant weights that obey the importance distribution, and represent the posterior probability density based on the estimation of these samples. When the sample size is large enough, this probability estimate will be equivalent to the posterior probability density.

[0031] However, the optimal importance function usually has problems such as unanalyzable and difficult to sample. Therefore, the weight importance sampling theory can be used to construct the importance function. The traditional particle filter uses the state transition probability as the importance function, but because it does not use the latest observations of the system, the particles are heavily dependent on the model, so the sample deviation from the actual...

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Abstract

The invention relates to an improved particle filtering method based on the niche genetic algorithm. The method comprises the following steps of: (1) sampling based on the initial probability distribution to obtain initial particles and setting the initial weight; (2) based on the filtering estimations of M particles at (k-1)th moment, carrying out EKF or UKF on each sampled particle to obtain the mean value and the covariance matrix corresponding to the kth moment, and respectively sampling n particles from each disposal distribution by using Gaussian density as the proposal probability density and using the mean value and the covariance matrix of each particle as the mean value and the covariance matrix of the distribution to obtain a set formed by nM particles; wherein n and M are natural numbers; (3) respectively updating the weights of the Nm particles to obtain the weight of each particle; and (4) when the obtained particle set has particles are less than the effective sample capacity, resampling with the niche genetic algorithm. The invention improves the particle filtering, inhibits the degeneracy phenomena and the particle-lack problem caused by simple random resampling, and improves the diversity and the adaptability of the particles, thereby improving the performance accuracy of the particle filtering.

Description

technical field [0001] The invention relates to the field of nonlinear filtering algorithms, in particular to an improved particle filtering method of a niche genetic algorithm. Background technique [0002] Nonlinear filtering technology has been widely used in many fields including satellite navigation, target tracking, image recognition, economic analysis, etc. The first method proposed to solve the problem of nonlinear system estimation is the Extended Kalman Filter (EKF). It is also the most widely used and mature technology at present. With the expansion of research fields and the continuous improvement of engineering application standards, higher requirements are put forward for the state, parameter estimation accuracy, and performance of complex system environments. It is used to approximate the model, and it is based on the assumption of Gaussian noise, so its accuracy performance cannot meet the needs in many cases. In recent years, the Untracked Kalman Filter (U...

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Application Information

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IPC IPC(8): G06K9/00G06N3/00
Inventor 秦红磊丛丽李子昱
Owner BEIHANG UNIV
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