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Multi-target tracking method based on variational Bayesian label multi-Bernoulli superposition model

A variational Bayesian, multi-target tracking technology, applied in the field of multi-target tracking based on the Variational Bayesian label multi-Bernoulli superposition model

Active Publication Date: 2019-11-26
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0004] In view of the above problems, the present invention proposes a multi-target tracking method based on the variational Bayesian label multi-Bernoulli superposition model to solve the multi-target tracking problem in the real scene under the unknown measurement noise environment, which has good performance, The adaptability and robustness to the environment can meet the design requirements in engineering

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[0065] The effectiveness of the present invention will be described below in conjunction with the accompanying drawings and simulation examples.

[0066] Simulation conditions and parameters

[0067] Assuming that the motion mode of multiple targets is uniform motion, the state of the target is expressed as x=[x,y,v x ,v y ] T , where x and y respectively represent the coordinates in the x direction and y direction in the Cartesian coordinate system, v x ,v y represent the velocity in the x-direction and y-direction of each target, respectively. The state equation of the target is x k =Fx k-1 +Gw k ,in

[0068] T represents the sampling time interval.

[0069] The scene is composed of radio frequency sensors, and the received signal strength of each sensor pair is used as a measurement. The RF sensor network chooses N s = 20 sensors, so the total number of sensor pairs (measurement dimension) M z =N s (N s -1) / 2=190, generate M at each moment z measurement. T...

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Abstract

The invention belongs to the technical field of intelligent information processing, and relates to a multi-target tracking method based on a variational Bayesian label multi-Bernoulli superposition model. The noise covariance of the superposition model is estimated. On the basis of an original superposition model, the covariance of measurement noise is unknown, unknown parameters are estimated based on variational Bayes, the prediction and updating process of the superposition model marked with the multi-Bernoulli filter is achieved, state extraction is conducted, and therefore the tracking problem of the superposition model under unknown measurement noise is solved. The method has the characteristics of wide application range, strong robustness, high estimation precision and the like, caneffectively solve the problem of non-cooperation in an actual superposition model scene, realizes multi-target tracking and parameter estimation in a complex scene, can meet design requirements, andhas a good engineering application value.

Description

technical field [0001] The invention belongs to the technical field of intelligent information processing, and relates to a multi-target tracking method based on a variational Bayesian label multi-Bernoulli superposition model. Background technique [0002] Traditional multi-target tracking mainly uses data association technology to achieve tracking, such as integrated probabilistic data association algorithm, joint integrated probabilistic data association algorithm, and multi-hypothesis tracker. Most of these algorithms need to know the number of targets and the starting position of the target, and with the increase of the target dimension and measurement dimension, the amount of calculation will increase exponentially, and it is difficult to perform real-time and effective calculation of the target in complex scenarios. multi-target tracking. [0003] In recent years, tracking algorithms based on the theoretical framework of stochastic finite sets have received widesprea...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00H03H17/02
CPCH03H17/0201G06F2218/02
Inventor 李改有魏平王敏高林陈奕琪
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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