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Fuzzy C-means track correlation method based on unsupervised clustering

An unsupervised clustering and track association technology, applied in character and pattern recognition, radio wave measurement systems, instruments, etc., can solve problems such as the inability to achieve correct association of positioning data, achieve suppression and elimination of interference, and avoid track occurrence initial effect

Inactive Publication Date: 2018-12-25
THE 724TH RES INST OF CHINA SHIPBUILDING IND
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Problems solved by technology

[0005] The invention solves the problem that the passive positioning system cannot realize the correct association of a large number of positioning data during the period due to the influence of measurement error and random difference in the data association cycle, and provides a combined method based on the correction logic method and unsupervised clustering Track initiation and association, a fuzzy C-means track association method based on unsupervised clustering

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  • Fuzzy C-means track correlation method based on unsupervised clustering
  • Fuzzy C-means track correlation method based on unsupervised clustering
  • Fuzzy C-means track correlation method based on unsupervised clustering

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[0014] The following combination figure 1 The present invention is described, and it should be understood that the drawings described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

[0015] A schematic diagram of a fuzzy C-means track association method based on unsupervised clustering of the present invention is as follows figure 1 shown, including the following steps:

[0016] s1. Perform DBSCAN clustering on the measurement set Z(1) obtained by the first scan to select a cluster center;

[0017] s2. Perform DBSCAN clustering on the measurement set Z(2) obtained in the second scan to select a cluster center;

[0018] s3. Use the FCM correlation method to judge the ambiguity of the two cluster centers. If the correlation ambiguity is greater than the threshold, it means that the two measurement sets are successfully associated, otherwise use the latest measurement set as the first measurement to resta...

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Abstract

The invention relates to a fuzzy C-means track correlation method based on unsupervised clustering. According to the problem that a great deal of passive positioning data cannot be correctly correlated in a period due to influence brought by measurement error and random difference in the data correlation period, a joint track initiation and correlation method based on a modified logic method and unsupervised clustering is presented, and the accuracy of track correlation is improved.

Description

technical field [0001] The technical invention belongs to the technical field of passive positioning track correlation filtering. Background technique [0002] With the rapid development of sensor technology, data fusion technology and network technology, multi-sensor systems have been widely used in both military and civilian applications. Among them, the passive detection sensor and its passive positioning system have good development potential because of their good concealment performance, and become the current research hotspot. [0003] The classic track association algorithms include nearest neighbor method, track bifurcation filter, generalized correlation method, Gaussian sum method, multi-model method, optimal BAYES method, multiple hypothesis method, probabilistic data association (PDA), joint probabilistic data association ( JPDA) and other different methods. Among them, the nearest neighbor method is the most concise and convenient, with a small amount of calcu...

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Application Information

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IPC IPC(8): G01S7/02G06K9/62
CPCG01S7/023G06F18/2321
Inventor 周俊秦坤翟晓宇
Owner THE 724TH RES INST OF CHINA SHIPBUILDING IND
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