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Multi-Feature Information Fusion Method for Target Data Association

A technology of target data and fusion method, which is applied in the field of target data association in the background of clutter, can solve problems such as tracking loss, multiple false tracks, and target mistracking, so as to improve reliability, reduce error association probability, and improve algorithm execution efficiency effect

Active Publication Date: 2018-04-17
NAVAL AERONAUTICAL & ASTRONAUTICAL UNIV PLA
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  • Claims
  • Application Information

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Problems solved by technology

[0006] In the background of dense clutter, the use of existing data association methods will produce more false tracks, especially when multiple targets are close, it is more likely to mistrack and lose the target

Method used

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Embodiment Construction

[0027] The following is attached with the manual figure 1 The present invention will be described in further detail. Refer to the instructions attached figure 1 , The specific implementation of the present invention is divided into the following steps:

[0028] (1) Preprocess the dot and trace data sent by the data recorder, mainly for the selection and information conversion of dots and traces, use the auxiliary information of dots and traces to delete the dots and traces that do not meet the conditions. The selection condition is: aggregation The effective number of points is greater than or equal to N, the degree of compactness is greater than or equal to P, N is a positive integer, and P is greater than 0 and less than 1, and finally all data is stored in frames. For example, N=100 and P=0.5 can be selected.

[0029] (2) According to the speed of the target, it is divided into three types: slow moving target, medium moving target, and fast moving target. The corresponding speed...

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Abstract

The invention relates to a multi-characteristic information fusion method for target data correlation and belongs to the technical field of radar data processing. The method comprises the following steps: 1) trace point pretreatment: screening data and storing the screened data according to frame; 2) target classification: classifying targets into three classes according to target speeds; 3) point-point correlation: carrying out track initiation by utilizing an m / n logical approach, and adopting different correlation strategies for different types of targets; 4) point-track correlation: carrying out extrapolation on a target track, and searching target candidate echoes according to a Bayesian data correlation algorithm; 5) feature similarity computation: extracting feature information of the candidate echoes and calculating feature similarity; and 6) comprehensive correlation degree computation: calculating comprehensive correlation degree according to the feature similarity and feature weight, and selecting the candidate echo, the comprehensive correlation degree of which is the largest, as target measurement. The method can reduce the number of the target candidate echoes, reduces operation burden, improves reliability of data correlation under a dense clutter background, and has a popularization and application value.

Description

Technical field [0001] The invention relates to the technical field of radar data processing, in particular to a target data association in a clutter background. Background technique [0002] Target tracking under the background of clutter is a current difficult problem. The core of target tracking is data association. Bayesian data association algorithms are widely used in engineering applications. Bayesian algorithms mainly include the following two categories: [0003] The first type only studies the latest set of confirmation measurements and is therefore a sub-optimal Bayesian algorithm, which mainly includes the nearest neighbor method (NN algorithm), probabilistic nearest neighbor algorithm (PNNF), and probabilistic data interconnection algorithm ( PDA), Joint Probabilistic Data Interconnection Algorithm (JPDA), etc. Among them, the NN algorithm and the PNNF algorithm are relatively simple correlation algorithms. They use the measurement closest to the predicted value in t...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S7/41
CPCG01S7/414
Inventor 黄勇张磊董云龙刘宁波朱红鹏李秀友姜佰辰张林关键
Owner NAVAL AERONAUTICAL & ASTRONAUTICAL UNIV PLA
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