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Particle filtering method based on Gaussian mixture model and variational Bayes

A Gaussian mixture model, variational Bayesian technology, applied in the field of signal processing, can solve problems such as inability to work, loss of work performance, etc.

Active Publication Date: 2017-05-17
SOUTH CHINA UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Signal processing methods based on Gaussian noise models will suffer a great loss in performance in non-Gaussian environments, or even fail to work

Method used

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  • Particle filtering method based on Gaussian mixture model and variational Bayes
  • Particle filtering method based on Gaussian mixture model and variational Bayes
  • Particle filtering method based on Gaussian mixture model and variational Bayes

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Embodiment

[0089] This embodiment provides a particle filter method based on Gaussian mixture model and variational Bayesian, such as figure 1 As shown in the flowchart of the method, the method includes the following steps:

[0090] 1. Use the Gaussian mixture model to model the observation noise, and initialize the probability density function p(x 0 ), the Gaussian mixture model formula is:

[0091]

[0092] where J represents the number of Gaussian terms of the Gaussian mixture model, α k,j Indicates the weight coefficient of the Gaussian term j at time k, Indicates that the mean is μ k,j , the covariance is Gaussian distribution;

[0093] 2. The probability density function p(x based on the initial state 0 ), randomly generate N initial particles, and N is used as a trade-off between calculation amount and estimation accuracy;

[0094] 3. Initialize the unknown parameters Ψ in the Gaussian mixture model of observation noise k The hyperparameter λ 0 , β 0 , m 0 , Σ 0a...

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Abstract

The invention discloses a particle filtering method based on a Gaussian mixture model and variational Bayes, comprising the following steps: (1) modeling observation noise using a Gaussian mixture model, and initializing the initial state; (2) randomly generating N initial particles based on the probability density function of the initial state; (3) initializing the super parameters of the unknown parameters in the Gaussian mixture model of observation noise; (4) generating sampling particles from a chosen importance reference function; (5) updating measurement, and calculating the particle weights according to the latest observation value and a particle weight iteration formula; (7) using a variational Bayesian method to get the distribution of the unknown parameters in the Gaussian mixture model by means of loop iteration; and (7) normalizing the particle weights, and re-sampling a particle set in view of particle degradation. Through the scheme, the filtering precision and the target state estimation performance are improved effectively.

Description

technical field [0001] The invention relates to the field of signal processing, in particular to a particle filter method based on a Gaussian mixture model and variational Bayesian. Background technique [0002] Particle filter implements recursive Bayesian filter through non-parametric Monte Carlo simulation method, which is suitable for any nonlinear system that can be described by state space model, and the accuracy can approach the optimal estimate. Particle filter is simple and easy to implement, and it provides an effective solution to the analysis of nonlinear dynamic systems, which has attracted extensive attention in the fields of target tracking, signal processing and automatic control. The state space model of particle filter can be described as: [0003] x k =f(x k-1 )+u k [0004] the y k =h(x k )+v k [0005] Where f(·), h(·) are state transition equation and observation equation respectively, x k is the system state, y k is the observed value, u k ...

Claims

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Application Information

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IPC IPC(8): G06T7/277
Inventor 陆湛赵智余卫宇
Owner SOUTH CHINA UNIV OF TECH
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