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Indoor multi-target tracking method using density-based fast search clustering algorithm

A clustering algorithm and multi-target technology, which is applied in the field of indoor multi-target tracking, can solve the problems of frequent changes of WiFi hotspots, unsuitability for multiple targets, and many preprocessing steps, so as to overcome the problem of wireless blind spots and the number of clusters. Accurate, good wall penetration effect

Active Publication Date: 2016-06-01
XIDIAN UNIV
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Problems solved by technology

This method also has many limitations and disadvantages: more preprocessing steps are required for the operation of the device, the WiFi hotspots in the indoor space change frequently, need to be updated continuously, the positioning accuracy is poor, and it is not suitable for tracking multiple targets

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  • Indoor multi-target tracking method using density-based fast search clustering algorithm
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  • Indoor multi-target tracking method using density-based fast search clustering algorithm

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Embodiment Construction

[0033] 1. Technical principles

[0034] The present invention uses ZigBee wireless nodes to cover all positions of the scene area under the cross network of wireless links as evenly as possible, and uses the increase or attenuation of the link signal strength value caused by the occlusion of the wireless link by people, and the target's position is estimated. After the link information data is collected, link fluctuation detection is first performed on the entire scene area to obtain the distribution map of all fluctuating links, and then the links are removed, leaving only the intersection points formed by the links. The scene area at this time There is only one point distribution coordinate system left, on this basis, use the density-based fast search clustering algorithm to get the number and position of the target, then use the hidden Markov model to correct the number of targets, and use the fuzzy The C clustering algorithm is used to correct the position of the target, ...

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Abstract

The invention discloses an indoor multi-target tracking method using a density-based fast search clustering algorithm, and mainly solves the problems that the present indoor positioning technology has bad precision and cannot track multiple targets. The technical scheme comprises the steps of: 1) obtaining wireless link information and performing pre-treatment, and obtaining a fluctuation link set; 2) removing outliers in fluctuation link intersection points; 3) clustering normal points and obtaining the number and positions of the targets; 4) correcting the target number by using a Hidden Markov Model; 5) correcting the target positions by using fuzzy C clustering; 6) joining the number and position information of the targets and generating target moving tracks; and 7) performing multi-particle filtering correction on the target moving tracks and realizing accurate tracking on the targets. The indoor multi-target tracking method using the density-based fast search clustering algorithm reduces the influence of environment on the positioning precision, and improves the robustness. The precision can reach a centimeter level. The indoor multi-target tracking method using the density-based fast search clustering algorithm can be used for the indoor multi-target tracking and monitor region security.

Description

technical field [0001] The invention belongs to the technical field of computers, and further relates to an indoor multi-target tracking method, which can be used for indoor multi-target tracking and monitoring area safety. Background technique [0002] Indoor multi-target tracking is to use radio frequency signals to perform indoor positioning and track tracking of multiple targets. Existing indoor multi-target tracking methods can be roughly divided into three categories: indoor multi-target tracking methods based on non-portable devices, indoor multi-target tracking methods based on portable devices, and indoor multi-target tracking methods based on special equipment. Indoor multi-target tracking based on portable devices has many restrictions and a single scene adaptability. Indoor multi-target tracking based on special equipment requires additional customization, which is expensive and not suitable for commercial development. [0003] The clustering algorithm based on...

Claims

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Application Information

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IPC IPC(8): G01S5/02
CPCG01S5/0294
Inventor 杜军朝刘惠李瑞陈福山杨雪刘思聪李易锴
Owner XIDIAN UNIV
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