Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (2): 262-268.doi: 10.21629/JSEE.2018.02.06

• Electronics Technology • Previous Articles     Next Articles

Multi-sensor optimal weighted fusion incremental Kalman smoother

Xiaojun SUN(), Guangming YAN*()   

  • Received:2017-01-03 Online:2018-04-26 Published:2018-04-27
  • Contact: Guangming YAN;
  • About author:SUN Xiaojun was born in 1980. She received her Ph.D. degree from Heilongjiang University. She is an associate professor in Heilongjiang University. Her research interests are multi-sensor information fusion, state estimation, and system identification. E-mail:|YAN Guangming was born in 1979. He received his M.A. degree from Heilongjiang University. He is a a lecturer in Heilongjiang University. His research interests are information fusion and optimal estimation. E-mail:
  • Supported by:
    the National Natural Science Foundation of China(61104209);the National Natural Science Foundation of China(61503126);This work was supported by the National Natural Science Foundation of China (61104209; 61503126)


In practical applications, the system observation error is widespread. If the observation equation of the system has not been verified or corrected under certain environmental conditions, the unknown system errors and filtering errors will come into being. The incremental observation equation is derived, which can eliminate the unknown observation errors effectively. Furthermore, an incremental Kalman smoother is presented. Moreover, a weighted measurement fusion incremental Kalman smoother applying the globally optimal weighted measurement fusion algorithm is given. The simulation results show their effectiveness and feasibility.

Key words: weighted fusion, incremental Kalman filtering, poor observation condition, Kalman smoother, global optimality