Multi-sensor GM-PHD adaptive sequential fusion multi-target tracking method

A multi-target tracking and multi-sensor technology, which is applied in the field of multi-sensor GM-PHD adaptive sequential fusion multi-target tracking, achieves the effects of small calculation amount, wide application and clear configuration structure

Pending Publication Date: 2021-11-19
HANGZHOU DIANZI UNIV
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Problems solved by technology

[0004] An object of the present invention is to address the deficiencies in the prior art, and propose a multi-sensor GM-PHD adaptive sequential fusion multi-target tracking method based on cumulative amplitude likelihood, which solves the problem of multiple targets in dense clutter environments. An optimization problem for sensor fusion sequence that improves target estimation accuracy and maintains track

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  • Multi-sensor GM-PHD adaptive sequential fusion multi-target tracking method
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[0019] The specific implementation manner of the present invention will be described in detail below in combination with the technical scheme and accompanying drawings.

[0020] Such as figure 1 As shown, the multi-sensor GM-PHD adaptive sequential fusion method based on cumulative magnitude likelihood is characterized in that the method specifically includes the following steps:

[0021] Step (1), build a multi-sensor multi-target tracking scene, and initialize the motion model of the target, set the relevant parameters of the target motion, including the process noise of the target motion and the measurement noise of the sensor; wherein the measurement of the sensor comes from the target or from clutter;

[0022] The motion model under the linear discrete system is expressed as:

[0023] x k = F x k-1 +B·μ k +ω k (1)

[0024] where x k is the state vector of the target at time k, F is the target state transition matrix, B is the control matrix, μ k is the motion co...

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Abstract

The invention discloses a multi-sensor GM-PHD adaptive sequential fusion multi-target tracking method. In order to solve the problem of multi-sensor multi-target tracking under dense clutters, the invention provides a set of complete processing method flow, amplitude information is introduced into a GM-PHD filter, original measurement is screened through amplitude characteristics of targets and clutters and by setting a detection threshold, a large amount of clutters are eliminated, effective measurement is obtained, the invention further provides a fusion sequence optimization method based on the effective measurement cumulative amplitude likelihood ratio, and multi-sensor fusion is carried out in combination with a distributed sequential fusion framework. The method is clear in configuration structure and small in calculation amount, and can be widely applied to the field of multi-target tracking.

Description

technical field [0001] The invention belongs to the multi-target tracking technology field of multi-sensor fusion in a complex environment, and relates to a multi-sensor GM-PHD adaptive sequential fusion multi-target tracking method, specifically a multi-sensor adaptive fusion multi-target based on probability assumption density filtering The tracking method is used to solve multi-target tracking under dense clutter, improve the tracking effect of unknown targets in the monitoring area, and achieve high-precision and stable tracking results. Background technique [0002] In a multi-sensor tracking system, data fusion technology needs to fuse data from multiple sensors to obtain state estimation of the target, which can improve the performance of the tracking system. However, with the increase in the number of targets and the complexity of data association, multi-sensor multi-target tracking technology also faces many challenges. So far, domestic and foreign researchers have...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/00G06F30/20
CPCG06F30/20G06F2218/02G06F18/25
Inventor 申屠晗张浩野荣英姣郭云飞彭冬亮
Owner HANGZHOU DIANZI UNIV
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