A Gaussian Mixture Unscented Particle Filter Algorithm Using Adaptive Resampling

A technology of unscented particle filtering and Gaussian mixing, which is applied in computing, image data processing, instruments, etc., and can solve problems such as particle filtering theory and algorithms are not perfect

Inactive Publication Date: 2018-02-02
BEIHANG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] Non-linear and non-Gaussian state estimation problems widely exist in various scientific researches and engineering practices. Particle filtering provides an effective solution to such problems, but the development of particle filtering theory and algorithms is not perfect enough. , there are many problems to be improved, so the in-depth study of the particle filter algorithm has important theoretical significance and broad application prospects

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  • A Gaussian Mixture Unscented Particle Filter Algorithm Using Adaptive Resampling
  • A Gaussian Mixture Unscented Particle Filter Algorithm Using Adaptive Resampling
  • A Gaussian Mixture Unscented Particle Filter Algorithm Using Adaptive Resampling

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Embodiment Construction

[0073] Specific examples of the present invention are given below to illustrate the effectiveness of the present invention.

[0074] Consider the following dynamic state-space model of a nonlinear discrete-time system:

[0075]

[0076] Where s k ∈R n Is the system state vector, in the known initial state distribution p(s 0 In the case of ), it is propagated in time through the system state function f(·). z k ∈R m Is a conditionally independent observation vector. In a given state, according to the observation likelihood function p(z k |s k )produce. f k :R n ×R r →R n Is the nonlinear state function of the system. h k :R n ×R p →R m Is the observation function of the system. w k-1 ∈R r , V k ∈R p They are system process noise and observation noise.

[0077] State space model f k :

[0078] In order to prove the excellent performance of the present invention in a nonlinear system, consider the state function f k Dynamic model with random walk:

[0079]

[0080] Where Δt is the sampl...

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Abstract

The invention relates to a Gaussian mixture unscented particle filter algorithm employing adaptive resampling. The common single-Gaussian particle filter algorithm is optimized in two aspects, namely importance density function and importance resampling. Gaussian mixture unscented transformation is used as the importance density function for particle filtering, and system state can be estimated accurately. On the basis of traditional residual resampling, the adaptive residual resampling simple and effective is provided in order that particle degeneracy and particle impoverishment are relieved. An uncertainty model, random surfer model, is used as a state model is used in order to estimate the performance of the algorithm. Simulation results show that the algorithm is superior to the common single-Gaussian particle filter algorithm in terms of tracking precision, state estimation and particle set diversity.

Description

Technical field [0001] The invention relates to the field of nonlinear filtering algorithms, in particular to a Gaussian mixture unscented particle filtering method adopting adaptive residual resampling, which is applied to target tracking in the image field. Background technique [0002] Non-linear filtering has always been a research hotspot in the field of image processing and artificial intelligence. It has important application value in the fields of intelligent monitoring, automatic control, navigation, financial management data analysis, maneuvering target tracking, economic statistics, and digital communications. With the increase in the complexity of the filtering tracking model and the continuous improvement of the demand for filtering accuracy, the traditional nonlinear filtering methods can no longer meet the actual requirements. As a new type of nonlinear filtering method, particle filter is not limited by the characteristics of system model and noise distribution, a...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/277
CPCG06T2207/30241
Inventor 张娜杨昕欣王新忠于正泉
Owner BEIHANG UNIV
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