Paralleling gauss particle filtering method based on quasi-Monte Carlo sampling

A Gaussian particle filter and quasi-Monte Carlo technology, applied in the field of signal processing, can solve problems such as difficult parallel implementation, degradation of estimation performance, particle degradation, etc., to achieve the effect of ensuring statistical relationship, improving accuracy, and improving stability

Inactive Publication Date: 2009-05-20
XIDIAN UNIV
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Problems solved by technology

The disadvantage of this basic particle filter method is that there is particle degradation, which is generally improved by an algorithm called "resampling", but this kind of resampling not only takes up a lot of computer resources but is not easy to implement in parallel
[0005] Since both the basic particle filter and the Gaussian particle filter are based on Monte Carlo sampling, the sampled particles tend to form "gaps and clusters" in

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  • Paralleling gauss particle filtering method based on quasi-Monte Carlo sampling
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  • Paralleling gauss particle filtering method based on quasi-Monte Carlo sampling

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[0029] The filtering method proposed by the present invention is implemented by establishing a dynamic model of a nonlinear system. The specific model is as follows:

[0030] State equation: X t =f(X t-1 )+w t (1)

[0031] Observation equation: Z t =h(X t )+e t (2)

[0032] Among them, f(·), h(·) are bounded nonlinear functions, X t Is the state of the system at time t, Z t Is the observation value of the system at time t; w t Is process noise, e t To observe the noise.

[0033] The parameters involved in the filtering method of the present invention include:

[0034] N: is the number of samples, P=2 k : Is the number of parallel units, p=1, 2,..., P: is the serial number of the parallel units.

[0035] Reference figure 1 The method of the present invention includes a parallel quasi-Monte Carlo sequence generation step and a parallel Gaussian particle filter step.

[0036] 1. Parallel quasi-Monte Carlo sequence generation steps

[0037] The jump quasi-Monte Carlo sequence g...

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Abstract

The invention discloses a method for filtering parallel Gauss particles based on quasi-Monte Carlo sampling, belongs to the technical field of signal processing, and mainly solves the problem that the prior filtering method based on the quasi-Monte Carlo sampling reduces performance for estimating state of a nonlinear dynamic system when the sample number is less. The filtering method comprises: a generating step of a parallel quasi-Monte Carlo sequence and a filtering step of the parallel Gauss particles, namely adopting a generating method for jumping the quasi-Monte Carlo sequence first, and generating a P quasi-Monte Carlo random sample sequences required by subsequent filtering in parallel; then converting each quasi-Monte Carlo random sample sequence into a quasi-Gauss sample sequence obeying designated Gaussian distribution through P execution units; at the same time, updating the time and the state for the state of the nonlinear dynamic system; and finally carrying out comprehensive treatment on all the execution units to finish filtering the nonlinear dynamic system. The method has the advantages of high filtering performance and good real-time property, and can be used in the field of signal processing, automation and artificial intelligence.

Description

technical field [0001] The invention belongs to the technical field of signal processing and relates to nonlinear filtering, in particular to a parallel Gaussian particle filtering method for state estimation of nonlinear dynamic systems. Background technique [0002] Nonlinear filtering techniques are widely used in many fields such as signal processing, automation, computer vision, and artificial intelligence. Extended Kalman filter EKF is a classical method of nonlinear filtering. However, since this extended Kalman filter replaces the nonlinear function with its second-order Taylor expansion, it will generate approximation errors and low precision, which will easily cause filter divergence in application. Unscented Kalman filter UKF is another method of nonlinear filtering. Unscented Kalman filter does not use a linearization method to approximate a nonlinear function, but uses a limited number of sample points that can fully describe the mean and variance of the state ...

Claims

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Application Information

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IPC IPC(8): G06K9/00
Inventor 姬红兵武斌陈曦
Owner XIDIAN UNIV
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