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A technology of radar networking and targeting, applied in the field of radar
Inactive Publication Date: 2016-05-04
PEOPLES LIBERATION ARMY ORDNANCE ENG COLLEGE
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[0004] At present, in the existing technology, the technology related to
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Embodiment 1
[0064] Assume that three radars in the existing radar network detect and identify an air target. Three target classes are known , , . Select four attribute variables for identification , , with , the standard values are , , with , and the standard deviation is , , with , the parameter templates of the three target categories are given by the prior knowledge, as shown in Table 1.
[0065] Table 1 Standard value and standard deviation of target category
[0066]
[0067] The measurement data records of each radar after space-time registration and correlation processing are shown in Table 2. Try to determine the category of the air target.
[0068] Table 2 Radar measurement data
[0069]
[0070] First, the leaf nodes of the class decision tree are formed from the prior information in Table 1.
[0071] Second, create the first node. Obtained from formula (2), the information entropy required to classify the measurement data set is . C...
Embodiment 2
[0079] The prior knowledge of the three object categories is shown in Table 1. Assuming that there are some blank values in the measurement data of the radar network, the blank values are arbitrarily set in the original measurement data to obtain Table 3. Try to determine the category of the air target.
[0080] Table 3 Radar measurement data (including blank values)
[0081]
[0082] The identification method is similar to that in Embodiment 1, and the same parts will not be described again. At the first node, the information gain of each attribute variable is calculated. Due to the presence of vacant values, only the information entropy required for the classification of non-null attribute values is calculated. will have the maximum information gain property As a classification attribute, classify the current node data set. get the degree of membership
[0083] ,
[0084] It can be seen from the degree of membership that the second group and the third grou...
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Abstract
The present invention discloses a radar networking identification target method, belonging to the field of the radar technology. The method comprises: s radars are configured to respectively measure n attributes of a detection target, forming s groups of radar measurement data, and belonging the detection target to one of the possible targets, wherein each attribute of each possible target has one specific value range. According to the invention, the anti-interference capability of an identification system is improved, the identification difficulty is reduced, and the identification accuracy is improved; the identification classification process of the method is performed from top to bottom, is more simple and reliable relative to the current technology, and has high popularization and application values.
Description
technical field [0001] The invention relates to the technical field of radar, in particular to a radar network recognition method for targets. Background technique [0002] The modern battlefield environment is becoming more and more complex. In the presence of various interferences, the target measurement information obtained by radar echo has great uncertainty, which makes it difficult to meet the needs of combat system target recognition. For this reason, a target fusion recognition method based on radar networking has appeared in the prior art. This method uses multiple radars to detect a target at the same time and fuses the detection results to obtain a measurement result that is more accurate than a single radar. This radar network helps to enhance the system's anti-jamming capability and environmental adaptability, improves the credibility and accuracy of target recognition, and provides an important basis for combat command auxiliary decision-making. [0003] The ...
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IPC IPC(8): G01S7/41
CPCG01S7/41
Inventor 韩壮志尚朝轩王品王雪飞胡文华解辉刘利民
Owner PEOPLES LIBERATION ARMY ORDNANCE ENG COLLEGE
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