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Multi-sensor GMPHD adaptive fusion method based on OSPA iteration

A multi-sensor, fusion method technology, applied in the field of multi-target tracking, can solve the problem of multi-sensor data fusion sequence sensitivity, and achieve the effect of clear configuration structure and small calculation amount

Active Publication Date: 2020-06-26
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

As Meyer pointed out, sequential fusion methods are very sensitive to the order of multi-sensor data fusion due to some information loss in each fusion cycle

Method used

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  • Multi-sensor GMPHD adaptive fusion method based on OSPA iteration
  • Multi-sensor GMPHD adaptive fusion method based on OSPA iteration
  • Multi-sensor GMPHD adaptive fusion method based on OSPA iteration

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Embodiment Construction

[0016] The specific implementation manner of the present invention will be described in detail below in combination with the technical scheme and accompanying drawings.

[0017] Such as figure 2 As shown, a multi-sensor GMPHD adaptive fusion method based on OSPA consistent iteration is as follows:

[0018] (1) Build a multi-sensor multi-target tracking scene, initialize the target motion model, set the relevant parameters of the target motion, including the process noise of the target motion and the measurement noise of the sensor; the measurement of the sensor comes from the target or from the noise Wave;

[0019] Create a motion model for the target:

[0020] In the formula, k represents the discrete time variable, i represents the serial number of the target, i=1,2,...,N, Indicates the state variable of the i-th target at time k, ω k Indicates that the mean is zero and the variance is Q k Gaussian white noise, mapping f k|k+1 State transition equation representing...

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Abstract

The invention discloses a multi-sensor GMPHD self-adaptive fusion method based on OSPA iteration. In order to research the influence of a fusion sequence on a fusion result, based on a measurement iteration correction multi-sensor PHD (ICMPHD) algorithm and based on an OSPA measurement evaluation index, an adaptive iterative correction multi-sensor PHD (AICMPHD) method is provided, and then a Gaussian mixture (GM) technology is introduced into the AICMPHD method to realize an AIC-GMPHD algorithm. The method is clear in configuration structure and small in calculation amount, and can be widelyapplied to the field of multi-target tracking.

Description

technical field [0001] The invention relates to the multi-target tracking field of multi-sensor fusion in complex environments, and relates to a multi-sensor self-adaptive fusion multi-target tracking method based on probability hypothesis density filtering, which is used to solve multi-target tracking in complex environments and improve monitoring accuracy. The tracking effect of unknown targets in the area can achieve high-precision and stable tracking effect. 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 proposed many data fusion algorithms, mainly including two types: sensor-level f...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/277G06K9/62G01C21/00G01C25/00
CPCG06T7/277G01C21/00G01C25/00G06F18/25
Inventor 申屠晗朱袁伟郭云飞薛安克石义芳
Owner HANGZHOU DIANZI UNIV
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