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A target clustering method based on a self-organizing feature mapping network

A feature mapping and self-organizing technology, applied in intention recognition and command and control systems, target grouping based on self-organizing feature mapping networks, and situation estimation, which can solve problems such as unstable grouping results and limited global optimization capabilities.

Active Publication Date: 2019-05-17
AIR FORCE UNIV PLA
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Problems solved by technology

[0006]The genetic algorithm is a classic intelligent algorithm, which is widely used in engineering, but it needs to set the number of groups in advance, and due to the limited global optimization ability, the grouping results may be unstable question

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  • A target clustering method based on a self-organizing feature mapping network
  • A target clustering method based on a self-organizing feature mapping network
  • A target clustering method based on a self-organizing feature mapping network

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Embodiment Construction

[0072] The present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments.

[0073] refer to figure 1 , the target grouping method based on self-organizing feature map network of the present invention, specifically comprises the following steps:

[0074] Step 1. Read data

[0075] 1.1) Let the initial time k=1, read the type of the t-th target at time k course Location and speed The value of t is 1, 2, ..., N k , N k is the total number of targets at time k;

[0076] 1.2) In order to facilitate the description of the target grouping problem, the t-th target sensor data at time k uses a one-dimensional vector said, among them Indicates the tth target attribute at time k, Indicates the t-th target type at time k, Indicates the heading of the t-th target at time k, Indicates the tth target position at time k, Indicates the speed of the t-th target at time k, and the data set of all target sensors at...

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Abstract

The invention discloses a target grouping method based on a self-organizing feature mapping network. The target grouping method comprises the following steps of reading the data obtained by a sensor at the current moment; cleaning the read sensor data; introducing an SOM to group the processed data, calculating the distance between the neurons and the sensor data by using a hybrid calculation method, and checking the accuracy of the group by using a standardized confidence value; evaluating the target grouping condition, and timely correcting according to the actual condition; and outputting atarget clustering result, and repeating the process. According to the method, by carrying out data cleaning before target grouping, noise interference is effectively filtered, and the accuracy of thetarget grouping is improved, the difference between targets can be effectively reflected, and the accuracy of target grouping is improved. By introducing the SOM, the key problem that the grouping number needs to be specified in advance and the threshold value needs to be set is solved, the accuracy and speed of target grouping are improved, and the requirements of practical application are met.By introducing a CV to check the target grouping condition, the robustness of the algorithm is improved.

Description

technical field [0001] The present invention relates to the field of situation estimation, in particular to a target grouping method based on a self-organizing feature map network (Self-Organizing Feature Map, SOM), which can be used in situation estimation, intention recognition and command and control systems. Background technique [0002] Target grouping is to reliably and effectively group target information of similar types, similar data and sourced from multiple sensors, which can improve information recognition, solve the problem of dazzling information, and help quickly grasp the situation and make correct decisions. [0003] At present, typical target grouping methods include K-means, hierarchical clustering methods, and genetic algorithms. in: [0004] The K-means method is easy to implement, but it needs to pre-specify the number of clusters, which is inconsistent with the actual situation, and the grouping results are related to the initial cluster center, so th...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04
CPCY02D30/70
Inventor 黄震宇白娟张振兴杨任农王栋
Owner AIR FORCE UNIV PLA
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