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A clustering method based on fuzzy c-means dot traces

A mean value and clustering technology, applied in instruments, measuring devices, using re-radiation, etc., can solve problems such as poor tracking effect and achieve high fault tolerance.

Active Publication Date: 2022-06-28
XIDIAN UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] To sum up, the problem existing in the existing technology is: when the target is relatively dense, the measurement values ​​of multiple targets will be relatively close to each other, and may be divided into one cluster when clustering. When estimating the number of targets and estimating target parameters Large error occurs when different target measurement values ​​may be divided into different clusters, resulting in poor tracking effect; in the case of densely expanded targets, the tracking effect is poor

Method used

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  • A clustering method based on fuzzy c-means dot traces
  • A clustering method based on fuzzy c-means dot traces
  • A clustering method based on fuzzy c-means dot traces

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Experimental program
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Embodiment 1

[0083] When the targets are denser, the measurement values ​​of multiple targets will be relatively close, and may be divided into one cluster during clustering, which will cause large errors in estimating the number of targets and estimating target parameters. Coupled with the false alarm measurements generated by nearby electromagnetic interference, it is difficult to meet the requirements. like figure 1 As shown, the fuzzy C-means-based clustering method provided by the embodiment of the present invention specifically includes the following steps:

[0084] (1) Grouping of measured values

[0085] According to the predicted value of the target, some elliptical wave gates are defined in the detection area, each target corresponds to an elliptical wave gate, and the measured value in the elliptical wave gate indicates that the measured value may be generated by the target at this moment. Suppose there are N k Tracks exist in the kth frame, resulting in a possible coincidenc...

Embodiment 2

[0120] The method for clustering based on the fuzzy C-mean point trace provided by the embodiment of the present invention is the same as that of Embodiment 1, and the specific steps of estimating the number of targets in the group described in step (3) are as follows:

[0121] Suppose the possible value of the target number c in a group is c 1 , c 2 ,...,c m and c 1 ≤c 2 ≤...≤c m . The number of targets predicted at these elliptical gates is c 0 . The measurement rates of the targets are respectively γ 1 , γ 2 , ..., γ c0 , the number of measurements produced by the target can be viewed as a Poisson distribution. Therefore, the target number is equal to c i The probability estimate of is:

[0122]

[0123] If P(c i ) is not greater than the constant threshold, then discard c i . For the efficiency of the algorithm, the selected partitions with small probability will be discarded. Then, the remaining selection partitions are calculated step by step.

Embodiment 3

[0125] The method for clustering based on the fuzzy C-mean point trace provided by the embodiment of the present invention is the same as that of the embodiment 1-2, and the specific steps of selecting the initial center described in step (4) are as follows:

[0126] Suppose that after clutter removal, there are m measurements p k 1 , p k 2 ,...,p k m exists in the group. c 0 The predicted position of each target is p′ k 1 , p′ k 2 ,...,p' k c0 , γ min , γ max is the target measurement rate γ 1 , γ 2 , ..., γ c0 the minimum and maximum values. When the predicted position and the measured value agree well, the predicted position is taken as the initial center. The kernel density estimate for the predicted location is:

[0127]

[0128] Density threshold τ for the ith target i 'for:

[0129]

[0130] σ' is a normalization constant.

[0131] when greater than τ i ', the predicted position agrees with the measured value. if c 0 equal to c i , then...

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Abstract

The invention belongs to the technical field of radar tracking system; similar systems, and discloses a method based on fuzzy C-mean point trace clustering, grouping measurement values; removing clutter; estimating the number of targets in the group; selecting the initial center; calculating the membership matrix U t ; Perform defuzzification; estimate the integrity of the cluster; update the clustering matrix U t+1 and the center of the cluster v i ; compare U with the matrix norm t and U t+1 ;if ||U t+1 -U t ||≤ε, stop. Otherwise, set t=t+1 to perform a new round of updating; finally defuzzify the measured values ​​according to the clustering matrix. In order to find the initial target center, the present invention takes into account the predicted localization and survey rate. At the same time, the integrity of the clustering is considered in the iterative process of the FCM algorithm. Compared with the traditional method, the present invention has better robustness and effectiveness, and can be used to correctly cluster the multi-moving targets detected by the radar, so as to track the targets better.

Description

technical field [0001] The invention belongs to a radar tracking system; similar to the technical field of the system, in particular to a point trace clustering method based on Fuzzy C-means (Fuzzy C-means algorithm referred to as FCM algorithm), and a radar multi-maneuvering target detection system. Background technique [0002] Currently, the state-of-the-art techniques commonly used in the industry are as follows: The detection of radar multi-maneuvering targets has always been a challenging problem because the number of targets is unknown and time-varying. Due to the low resolution of previous radars, the target only appeared in a single resolution unit; with the improvement of the resolution of modern radars, the radar beam can collect measurements from multiple reflection points of the aircraft, that is, multiple measurements for one target value. A target with multiple detections is called an "extended target" or "extended object", in which case the target is no long...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S13/66G01S7/41
CPCG01S7/41G01S7/414G01S13/66
Inventor 许录平阎博滕欣进丁智青许娜杨升李沐青孙志峰周钇辛吕鹏飞
Owner XIDIAN UNIV
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