Data Prediction Compensation for Dynamic Target Tracking System Based on BP Neural Network

Shuqing Xu, Yongrui Qin, Haiyin Zhou, Bowen Sun, Jiongqi Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


During the target tracking process, some observation data may be missing due to the equipment problems or the operation errors, which may affect the filtering process of the target state and the position determination accuracy. Therefore, the missing data needs to be effectively compensated. This paper provides a method to compensate the missing data by using the characteristics of BP neural network learning system aiming at the dynamic target tracking system. The neural network is trained by using the complete data of the dynamic system, and then the missing data is predicted by the trained neural network. The simulation results for both of the linear system and the nonlinear system show that the method is indeed effective, compared to the traditional time update prediction, the prediction accuracy is much higher.

Original languageEnglish
Title of host publicationProceedings 2018 Chinese Automation Congress
Subtitle of host publicationCAC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728113128
ISBN (Print)9781728113135
Publication statusPublished - 24 Jan 2019
Event2018 Chinese Automation Congress - Xi'an, China
Duration: 30 Nov 20182 Dec 2018


Conference2018 Chinese Automation Congress
Abbreviated titleCAC 2018


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