Evaluation of nonlinear filtering for radar data tracking

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• 作者：Yanan Liu (1)
  Sese Wang (2)
  Zhuo Sun (2)
  Jihong Shen (3)
  
  1. College of Automation ; Harbin Engineering University ; No.145 Nantong Street ; Nangang District ; Harbin ; 150110 ; China
  2. Key Laboratory of Universal Wireless Communication ; Ministry of Education ; Beijing University of Posts and Telecommunications ; No.10 Xitucheng Road ; Haidian District ; Beijing ; 100876 ; China
  3. Faculty of Science ; Harbin Engineering University ; No.145 Nantong Street ; Nangang District ; Harbin ; 150110 ; China
  
• 关键词：Radar tracking ; Kalman filter (KF) ; Nonlinear filter ; Unscented Kalman filter
• 刊名：EURASIP Journal on Wireless Communications and Networking
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：2015
• 期：1
• 全文大小：1,669 KB
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• 刊物主题：Signal, Image and Speech Processing;
• 出版者：Springer International Publishing
• ISSN：1687-1499

文摘

Radar tracking plays an important role in the area of early warning and detection system, whose precision is closely connected with filtering algorithm. With the development of noise jamming technology in radar echo signal, linear filtering becomes more and more difficult to satisfy the demands of radar tracking, while nonlinear filtering can solve problems such as non-Gaussian noises. There exist a lot of nonlinear filtering algorithms at present, owning their particular characteristics. With this in mind, we provide a comprehensive overview of different nonlinear filtering algorithms in radar tracking, including basic ideas and concrete steps of them. For a more clear presentation, we also make comparisons of them from all sides. Through the analyses of different nonlinear data filters, we find that the unscented Kalman data filter (UKF) can achieve better performance than others. Therefore, we will simulate and show the performance of UKF, and performance of the extended Kalman data filter (EKF) under the same condition will be taken as comparison, whose accuracy was not ideal for radar tracking data filtering.