Non-linear dynamic system signal processing method based on sampling rejecting particle filter algorithm

A particle filter algorithm, a nonlinear dynamic technology, applied in electrical digital data processing, special data processing applications, calculations, etc., can solve the problems of lack of particles, particle weight degradation, inability to effectively handle nonlinear and non-Gaussian signals, etc. Avoid particle weight degradation, reducing the effect of running time

Inactive Publication Date: 2010-10-06
ZHEJIANG UNIV
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Problems solved by technology

[0005] In order to overcome the shortcomings of particle weight degradation and particle scarcity in the existing dynamic system signal processing method based on particle filter algorithm, and the inability to effectively process nonlinear and non-Gaussian signals, the present invention

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  • Non-linear dynamic system signal processing method based on sampling rejecting particle filter algorithm
  • Non-linear dynamic system signal processing method based on sampling rejecting particle filter algorithm
  • Non-linear dynamic system signal processing method based on sampling rejecting particle filter algorithm

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Embodiment Construction

[0038] The present invention will be further described below in conjunction with drawings and embodiments.

[0039] refer to Figure 1 to Figure 5 , a nonlinear dynamic system signal processing method based on the particle filter algorithm of rejected sampling, using particles to describe the state space of the dynamic system, the state space model of the nonlinear dynamic system is set as:

[0040] x k =f(x k-1 )+v k-1

[0041] z k =h(x k )+n k

[0042] where x k and z k Respectively represent the state and observation value of the system at time k, f(x k-1 ) and h(x k ) represent the state transition equation and observation equation of the system respectively, v k-1 and n k represent system noise and observation noise, respectively;

[0043] For each newly sampled particle, first calculate its probability of being accepted, and then judge whether it is accepted. The recursive process includes the following steps:

[0044] In the first step, according to the N...

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Abstract

The invention relates to a non-linear dynamic system signal processing method based on sampling rejecting particle filter algorithm, which comprises the following steps that: the state space of a particle description state system is adopted, the state space model of a non-linear dynamic system is set to be: xk=f(xk-1)+vk-lzk=h(xk)+nk, wherein xk and zk respectively refer to the state and the observation value of the system at k, f(xk-l) and h(xk) respectively refer to the state transition equation and the observation equation of the system, and vk-l and nk respectively refer to the system noise and the observation noise; and for each newly sampled particle, firstly the probability that the particle is accepted is calculated and then whether the particle is accepted is judged. The non-linear dynamic system signal processing method based on sampling rejecting particle filter algorithm can effectively reduce particle weight degradation, has good particle diversity, and effectively process non-linear and non-gaussian signals.

Description

technical field [0001] The invention relates to a signal processing method based on a particle filter algorithm, and the claimed technical solution belongs to the fields of signal processing, artificial intelligence and computer vision. Background technique [0002] The problem of state estimation for dynamical systems involves many fields, especially signal processing, artificial intelligence, and computer vision. The traditional Kalman filter is only suitable for linear Gaussian systems, and the extended Kalman filter can only deal with the weak nonlinearity of the system. Therefore, particle filters suitable for nonlinear and non-Gaussian systems have attracted much attention. [0003] Particle filter is a filtering method based on Monte Carlo simulation and recursive Bayesian estimation. It uses particles to describe the state space, uses a group of weighted particles to approximate the posterior probability density of the system, and realizes the recursive estimation ...

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Application Information

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IPC IPC(8): G06F17/00
Inventor 潘赟郑宁严晓浪王一木孙纲德
Owner ZHEJIANG UNIV
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