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Artificial fish-swarm based particle filtering method

A particle filter and artificial fish swarm technology, applied in the direction of instruments, computing models, biological models, etc., can solve the problems of particle degradation, particle loss of diversity, etc., to achieve the effect of improving particle distribution, increasing diversity, and improving filtering accuracy

Inactive Publication Date: 2012-02-22
JIANGSU UNIV OF SCI & TECH
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AI Technical Summary

Problems solved by technology

[0004] However, there are still many problems to be solved in particle filtering. Particle degradation is the main shortcoming of particle filtering. It refers to the phenomenon that particles lose diversity as the number of iterations increases.

Method used

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  • Artificial fish-swarm based particle filtering method
  • Artificial fish-swarm based particle filtering method
  • Artificial fish-swarm based particle filtering method

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Embodiment Construction

[0040] The state of the individual artificial fish can be expressed as a vector X=(x 1 , x 2 ,...x n ), where x i (i=1,...,n) is the variable to be optimized; the food concentration at the current position of the artificial fish is expressed as Y=f(X), where Y is the objective function value; the distance between artificial fish individuals is expressed as d i，j =||X i -X j ||; v represents the perceived distance of the artificial fish; s represents the maximum step size of the artificial fish; δ is the crowding factor; r is a random number between (0, 1).

[0041] The foraging behavior of the artificial fish can be described as: Let the current state of the artificial fish be X i , within its perception range (d i，j j , if Y i j Then take a step forward in this direction, if not satisfied, then move one step randomly, that is

[0042] If Y i j , X inext = X i + r ...

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Abstract

The invention provides an artificial fish-swarm based particle filtering method in which the artificial fish-swarm algorithm is introduced into particle filtering. The basic thinking of the method is as follows: the clustering and foraging behaviors of the artificial fish-swarm algorithm are introduced while the target function, namely the measurement function, is selected and the importance weight is adjusted to guide the prior particles to continuously move toward a high likelihood domain, thus improving particle distribution, increasing the diversity of the particles and improving the filtering precision of the algorithm; besides, in the artificial fish-swarm algorithm, when a domain for optimization is larger or is in an area which is flat in change, a part of artificial fishes randomly move aimlessly, thus affecting the optimization efficiency; and the invention further provides an adaptive step method to improve the randomness of artificial fish view selection, thus not only lightening the computational burden of the algorithm but also ensuring the algorithm convergence.

Description

technical field [0001] The invention combines an artificial fish swarm intelligent algorithm and a particle filter algorithm to propose an artificial fish swarm particle filter method, which belongs to the field of nonlinear system filtering. Background technique [0002] The particle filter developed in recent years has broken through the Kalman filter theory, and it has no restrictions on the system process noise and measurement noise. Particle filter uses the Monte Carlo method to solve the integral operation in Bayesian estimation based on the theorem of large numbers, and approximates the Bayesian estimation of nonlinear systems by predicting and updating the sampling set from the system probability density function. It has unique advantages and wide applications in parameter estimation and state filtering of linear and non-Gaussian time-varying systems. Particle filtering is an interdisciplinary subject between modern signal and information processing disciplines and s...

Claims

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Application Information

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IPC IPC(8): G06N3/00
Inventor 朱志宇李阳李冀张冰刘维亭魏海峰赵强袁文华
Owner JIANGSU UNIV OF SCI & TECH
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