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Multi-sensor Vine Copula heterogeneous information decision fusion method

A technology for fusion of heterogeneous information and decision-making, applied in instruments, scene recognition, computing, etc., can solve problems such as difficulty in fusion, large differences in observation dimensions and quality, and numerous types of sensor observation data

Active Publication Date: 2020-12-22
SICHUAN UNIV
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Problems solved by technology

[0020] In view of the above problems, the purpose of the present invention is to provide a multi-sensor Vine Copula heterogeneous information decision-making fusion method, which can improve the fusion performance of heterogeneous sensors, and overcome the large number of sensor observation data types, observation dimension and quality differences in complex application scenarios Large, difficult integration and other issues

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Embodiment Construction

[0061] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments. Such as figure 2 As shown, the flow chart of the algorithm of R-Vine Copula heterogeneous sensor information fusion in the target classification problem of the present invention, the method steps are as follows:

[0062] Step 1: Input the feature data, class labels and binary copula sets observed by the sensor;

[0063] The feature data is expressed as: the kth sensor in the jth feature vector observes a feature of u jk ;

[0064] The class label is expressed as: the i-th class label v of the target i ;

[0065] The set of binary copulas is expressed as: F={c s :n=1,...,S}; c s is the binary copula probability density function;

[0066] Step 2: Calculate the marginal probability density f under different class labels using non-parametric kernel density estimation method k (u jk |v i );

[0067] Step 3: Calculate the marginal...

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Abstract

The invention discloses a multi-sensor Vine Copula heterogeneous information decision fusion method, and belongs to the field of information fusion. According to the method, a heterogeneous sensor Vine copula decision fusion algorithm is provided, different types of sensor data are unified into the same event space through feature extraction and event driving, and an event set corresponding to a target is established based on data features and prior information of the target; and modeling is carried out on the correlation between sensor data feature events through Vine copula. According to themethod, the correlation between the heterogeneous information fusion feature events can be flexibly constructed, more accurate event joint probability distribution is generated, and the decision fusion performance of heterogeneous sensors is improved; numerical analysis shows that the new method provided by the invention has a better decision-making effect on a target classification and identification problem, and can fuse heterogeneous sensor information more scientifically and reasonably.

Description

technical field [0001] The invention relates to the technical field of object classification data processing, in particular to a multi-sensor Vine Copula heterogeneous information decision fusion method. Background technique [0002] Heterogeneous sensor information fusion has a larger dimension and time scope in the information of detection targets, can make full use of the complementary information of multi-source heterogeneous data to improve information quality, and has the advantage of reliability in target tracking and identification. In a complex air-space confrontation environment, there are many types of observation data, high observation dimensions and large quality differences, making fusion difficult. The technical bottleneck is that it is difficult to unify the information measurement space, especially the correlation between sensors is difficult to extract, which directly affects the performance of decision-making fusion, and it is difficult to meet the urgent ...

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Application Information

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IPC IPC(8): G06K9/62G06K9/00
CPCG06V20/13G06V2201/07G06F18/2415G06F18/253Y02T10/40
Inventor 沈晓静张美孟凡饮刘海琪张栩琪
Owner SICHUAN UNIV
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