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A multi-sensor non-sequential measurement asynchronous fusion method based on GM-PHD filtering

A multi-sensor, fusion method technology, applied in the direction of instruments, character and pattern recognition, computer parts, etc., can solve the problems of asynchronous fusion of non-sequential measurement, complex multi-target asynchronous information fusion, many sensors and few sensors, etc., to avoid Data association problem, easy iterative update, and the effect of ensuring convergence

Active Publication Date: 2019-06-14
ZHEJIANG UNIV
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Problems solved by technology

The above algorithm is mainly aimed at the asynchronous information fusion under a single target. Due to the data association problem involved, the multi-target asynchronous information fusion will be more complicated. At present, there are few studies on the multi-sensor multi-target asynchronous fusion problem, and there is no research on the random set framework. Application research of non-sequential measurement asynchronous fusion

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  • A multi-sensor non-sequential measurement asynchronous fusion method based on GM-PHD filtering
  • A multi-sensor non-sequential measurement asynchronous fusion method based on GM-PHD filtering
  • A multi-sensor non-sequential measurement asynchronous fusion method based on GM-PHD filtering

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Embodiment Construction

[0018] The implementation of the present invention will be described in detail below in conjunction with the accompanying drawings, and specific operation modes and implementation steps will be given.

[0019] Step 1: Establish a multi-sensor multi-target tracking model and initialize it

[0020] Under the framework of random finite sets, the states and measurements of multiple targets can be regarded as random finite sets, assuming t k There are N(k) states and M(k) measurements at any time, and the states and measurements of multiple targets can be expressed in a finite set form:

[0021] x k ={x k，1 , x k，2 ,...,x k，N(k)} (1)

[0022] Z k ={z k，1 ,z k，2 ,...,z k，N(k)} (2)

[0023] where x k，1 , x k，2 ,...,x k，N(k) are respectively the 1st, 2nd, ..., N(k) target states, z k，1 ,z k，2 ,...,z k，N(k) are the 1st, 2nd, ..., M(k) measurements respectively.

[0024] For a single target, the motion model is:

[0025] x k =F(k,k-1)x k-1 +v(k,k-1) (3)

[0026] in,...

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Abstract

The invention discloses a multi-sensor non-sequential measurement asynchronous fusion method based on GM-PHD filtering. The multi-sensor non-sequential measurement asynchronous fusion method can be used for solving the multi-target tracking problem of multi-sensor asynchronous non-sequential measurement based on radar, sonar and the like in a clutter environment. According to the method, a centralized feature level fusion strategy is adopted, a fusion center judges the measurement received in real time, and a fusion algorithm based on a GM-PHD filter is designed for two types of asynchronous measurement of sequential measurement and arrival lag measurement. Especially for arrival lag measurement, the GM-PHD filter is reasonably improved, the problems of reverse state prediction and negative time measurement updating under a random set framework are solved, and the secondary estimation of the target state is achieved. By means of the advantages of the stochastic set theory, the complexdata association problem in the asynchronous fusion problem is avoided, the method is simple in structure and is easy to achieve iteration updating, and the method has the important practical significance for solving the actual multi-sensor multi-target tracking problem.

Description

technical field [0001] The invention belongs to the field of multi-target tracking and multi-sensor fusion, and relates to an asynchronous fusion method for multi-sensor non-sequential measurement, which can be used to solve the problem of multi-target tracking based on multi-sensor asynchronous measurement such as radar and sonar in a clutter environment. Background technique [0002] Multi-sensor multi-target tracking technology has a wide range of military and civilian applications, such as air early warning, ocean monitoring, automatic driving, etc. With the continuous enrichment of sensor types and the effective improvement of accuracy, multi-target tracking methods including data fusion are also gradually developing. [0003] For the multi-target tracking problem, the classic solutions focus on solving the data association problem, mainly including the nearest neighbor algorithm (Nearest Neighbor, NN), the global nearest neighbor algorithm (Global Nearest Neighbor, GNN...

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Application Information

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IPC IPC(8): G06K9/62
Inventor 刘妹琴赵立佳张森林何衍
Owner ZHEJIANG UNIV
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