Data Mining-Based Tracking Method for Multisource Target Data of Heterogeneous Networks

Abstract

In order to solve the problem that the target is easily lost in the process of multisource target data fusion tracking, a multisource target data fusion tracking method based on data mining is proposed. Multisource target data fusion tracking belongs to location level fusion. Firstly, a hybrid heterogeneous network fusion model is established, and then, data features are extracted, and a fusion source big data acquisition algorithm is designed based on compressed sensing to complete data preprocessing to reduce the amount of data acquisition. Based on data mining association multisource fusion target, get the relationship between each measurement and target, and build multisource target data fusion tracking model to ensure the stable state of fusion results. It shows that the proposed method can save the tracking time and improve the tracking accuracy compared with the methods based on NNDA and PDA, which is more conducive to the real-time tracking of multisource targets.

Heterogeneous sensing for target tracking: architecture, techniques, applications and challenges

“…Heterogeneous network data fusion and integration refers to the synergistic combination of sensing data from multiple different types of sensors to provide more reliable and accurate target information for target tracking [155][156][157]. The advantages of sensor fusion and integration are redundancy, complementarity, timeliness and cost of information.…”

Retracted: Data Mining-Based Tracking Method for Multisource Target Data of Heterogeneous Networks

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”