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Based on OSPA (2) Multi-sensor multi-target track association and fusion method based on range

A fusion method and track correlation technology, applied in the field of track correlation and fusion, can solve problems such as the performance degradation of track correlation, and achieve the effects of avoiding the reduction of correlation, high precision, and reducing position error.

Active Publication Date: 2022-04-08
HANGZHOU DIANZI UNIV
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Problems solved by technology

[0004] The purpose of the present invention is exactly for the situation that a plurality of sensors track multi-target, the prior art only carries out the method of association according to the information of current track point, owing to lacking the consideration to the whole track feature, can cause the correlation between track performance degradation issues, providing an OSPA-based (2) A method of distance-to-multi-sensor multi-target track association and fusion

Method used

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  • Based on OSPA <sup>(2)</sup> Multi-sensor multi-target track association and fusion method based on range
  • Based on OSPA <sup>(2)</sup> Multi-sensor multi-target track association and fusion method based on range
  • Based on OSPA <sup>(2)</sup> Multi-sensor multi-target track association and fusion method based on range

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Embodiment Construction

[0027] The present invention will be further described below in conjunction with accompanying drawing.

[0028] Step (1) Use track-oriented multi-hypothesis tracking algorithm (MHT) to obtain the estimated track sets of I sensors and J targets respectively; establish a system model, consider a nonlinear multi-sensor system model with bias, and the system process noise is known , its state equation, deviation equation and measurement equation are mathematically described as: Among them, k is the time series, x k and x k+1 are the state vectors at time k and k+1 respectively, A k+1,k is the state transition matrix, b k and b k+1 is the input matrix at k time and k+1 time, B k+1,k is the coefficient matrix corresponding to the input matrix, is the observation vector of the i-th sensor at time k, is the state observation matrix of the i-th sensor at time k, is the coefficient matrix of the i-th sensor at time k, w k , are respectively the system state noise vector, ...

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Abstract

The invention discloses an OSPA-based (2) Multi-sensor multi-target track association and fusion method for distance. Existing methods lack the consideration of the characteristics of the entire track, resulting in a decrease in the correlation performance between tracks. The method of the present invention first establishes a system model, obtains the estimated track set; then based on OSPA (2) The distance measures the distance between each estimated track set, and the estimated track sets belonging to the same target are obtained; finally, the federated filter fusion method is used to fuse the estimated track sets belonging to the same target. The method of the invention can accurately obtain the estimated track set belonging to the same target, and the RMSE position error of the obtained final track is lower than that of all single sensors, and higher precision can be achieved.

Description

technical field [0001] The invention belongs to the field of track association and fusion, and relates to an OSPA-based (2) (OSPA on OSPA, Optimal Sub-Pattern Assignment on Optimal Sub-Pattern Assignment) Distance to multi-sensor multi-target track association and fusion method. Background technique [0002] In reality, the situation that multiple sensors track multiple targets frequently occurs, which results in the track of one target usually having multiple tracks. At the same time, due to the existence of space alignment, time alignment, and different target observation accuracy, there are often multiple sets of tracks obtained from multiple sensors. In the face of the above situation, how to accurately obtain the estimated track set belonging to the same target among many estimated track sets has become an important content. Conventionally, track correlation is performed based on the geometric distance between tracks to determine which tracks belong to the same target...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01C21/20
CPCG01C21/20
Inventor 刘宇刘伟峰李建宁
Owner HANGZHOU DIANZI UNIV
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