Journal of Systems Engineering and Electronics ›› 2020, Vol. 31 ›› Issue (6): 1167-1177.doi: 10.23919/JSEE.2020.000089

• DEFENCE ELECTRONICS TECHNOLOGY • Previous Articles     Next Articles

De-correlated unbiased sequential filtering based on best unbiased linear estimation for target tracking in Doppler radar

Han PENG(), Ting CHENG*(), Xi LI()   

  1. 1 School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
  • Received:2019-08-22 Online:2020-12-18 Published:2020-12-29
  • Contact: Ting CHENG;;
  • About author:|PENG Han was born in 1994. He received his B.S. degree from Southwest Jiaotong University, and M.S. degree from University of Electronic Science and Technology of China (UESTC). He has been studying at UESTC since 2017. His primary research interests include statistical signal processing, detection, data fusion, and tracking. E-mail:||CHENG Ting was born in 1982. She received her Ph.D. degree from University of Electronic Science and Technology of China (UESTC) and she is an associate professor and postgraduate tutor at UESTC, mainly engaged in radar resource management and array signal processing methods. E-mail:||LI Xi was born in 1995. She is currently a master student in University of Electronic Science and Technology of China. Her research interests are target tracking as well as radar resource management. E-mail:
  • Supported by:
    This work was supported by the Basic Research Operation Foundation for Central University (ZYGX2016J039)


In target tracking applications, the Doppler measurement contains information of the target range rate, which has the potential capability to improve the tracking performance. However, the nonlinear degree between the measurement and the target state increases with the introduction of the Doppler measurement. Therefore, target tracking in the Doppler radar is a nonlinear filtering problem. In order to handle this problem, the Kalman filter form of best linear unbiased estimation (BLUE) with position measurements is proposed, which is combined with the sequential filtering algorithm to handle the Doppler measurement further, where the statistic characteristic of the converted measurement error is calculated based on the predicted information in the sequential filter. Moreover, the algorithm is extended to the maneuvering target tracking case, where the interacting multiple model (IMM) algorithm is used as the basic framework and the model probabilities are updated according to the BLUE position filter and the sequential filter, and the final estimation is a weighted sum of the outputs from the sequential filters and the model probabilities. Simulation results show that compared with existing approaches, the proposed algorithm can realize target tracking with preferable tracking precision and the extended method can achieve effective maneuvering target tracking.

Key words: Kalman filter, best linear unbiased estimation (BLUE), measurement conversion, sequential filter