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Target predicting and tracking method based on probability graph model

A probabilistic graph model and target tracking technology, which can be used in measuring devices, image analysis, image data processing, etc., can solve problems such as the difficulty of EKF algorithm

Inactive Publication Date: 2010-04-14
MAOMING COLLEGE
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Problems solved by technology

However, the target motion in the actual system is a nonlinear non-Gaussian random process that contains a large number of uncertain factors, and the EKF algorithm is difficult to meet the requirements of practical applications. The principle of non-parametric representation of the Monte Carlo simulation method, PF is widely concerned because of its flexible adaptation to nonlinear dynamic models and multi-modal observation models, and its ability to solve nonlinear and non-Gaussian filtering problems

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  • Target predicting and tracking method based on probability graph model
  • Target predicting and tracking method based on probability graph model
  • Target predicting and tracking method based on probability graph model

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Embodiment

[0037] Probabilistic graphical models:

[0038] An undirected graph model is expressed as G=(V, E), where V represents a set of vertices (nodes), and E represents a set of edges (edges). Such as figure 1 As shown, each vertex s∈V represents a random variable x s , s∈V, the relationship between variables can be represented by the graph model structure. The variables represented by the undirected graph are considered to be discrete, and these variables x are Markov random variables related to the graph structure, and its distribution p(x) is expressed as:

[0039] p(x)=κ∏ s∈V Ψ s (x s )∏ (s，t)∈E Ψ st (x s , x t )

[0040] where κ is a normalization constant: Ψ s (x s ) is a vertex-compatible function that depends on the variable x s ; st (x s , x t ) is an edge compatible function, which depends on the variable x s and x t The connecting line (s, t). In general, the random variable x is an implicit variable to be sought and cannot be observed. Assume that the...

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Abstract

The invention discloses a target predicting and tracking method based on a probability graph model, which builds a target tracking model by a probability graph model modeling method, estimates the target position by the particle filtering prediction algorithm, and infers the possible positions of the target at any time by a distributed particle filtering tracking method, thus realizing the target tracking. The invention is not restricted by linearization error and Gaussian noise assumption, does not need to assume the distribution form of sample points, can deal with any density distribution and flexibly adapt to non-linear dynamic models and multimodal observation models of target tracking. The method can obtain higher overall predicting degree of accuracy and show better target tracking performance.

Description

technical field [0001] The invention relates to the field of wireless sensor networks, in particular to a target prediction and tracking method based on a probability graph model. Background technique [0002] A wireless sensor network (WSN) is a distributed network formed by a large number of randomly distributed low-cost, low-power miniature wireless sensor nodes through self-organization. It can sense, collect and monitor network coverage in real time. The information of the monitored objects in the region has broad application prospects in both military and civilian fields. Object tracking is an important application of wireless sensor network (WSN). WSN has the advantages of miniaturization of sensor nodes, low price, wireless communication, random deployment, self-organization, robustness and concealment, and is very suitable for positioning and tracking of maneuvering targets. However, WSN itself has significant characteristics such as limited node energy, limited s...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S5/02H04W84/18G06T7/20
Inventor 刘美陈政石王涛廖晓文
Owner MAOMING COLLEGE
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