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      A New Target-correlation Algorithm for Heterogeneous Sensors Based on Neural Network Classification

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          Abstract

          In the data fusion system composed of radar and infrared sensor installed in high speed of dynamic platform, the system error estimation and target correlation are dependent and are difficult very much. To solve the problem, a new target correlation algorithm based on pattern classification is proposed in the article according to the property of system errors variation. The approach realizes pattern classification by BP neural network. It needn’t estimate the system error and compensate it, and has a tolerance to system error. The experiment shows that the average correct probability for target-correlation in the data fusion between the above two kind of sensors is more than 86%.

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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 December 2012
          : 1
          : 4
          : 399-405
          Affiliations
          [1 ] Department of Electronic Engineering, Tsinghua University
          [2 ] School of Information and Electronic, Beijing Institute of Technology
          [3 ] Air Force Early Warning Academy of PLA
          Article
          7d9b2237be1640bbb67014ba50a8bc47
          10.3724/SP.J.1300.2012.20087

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Categories
          Technology (General)
          T1-995

          Remote sensing, Electrical engineering

          Data fusion, Target correlation, Neural network, Classification

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