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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2015, Vol. 36 ›› Issue (5): 1596-1605.doi: 10.7527/S1000-6893.2015.0001

• Electronics and Control • Previous Articles     Next Articles

A redundant measurement adaptive Kalman filter algorithm

ZHOU Qifan, ZHANG Hai, WANG Yanran   

  1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
  • Received:2014-06-27 Revised:2015-01-14 Online:2015-05-15 Published:2015-01-19
  • Supported by:

    Open Research Fund of The Academy of Satellite Application (2014_CXJJ-DH_06)

Abstract:

In order to solve the problems of current adaptive Kalman filter, this paper proposes a redundant measurement adaptive Kalman filter (RMAKF) in the situation that the measurement noise variance is unknown. This method could accurately estimate the statistical characteristics of measurement noise through calculating the first and second order difference sequences and adaptively tuning the variance matrix of measurement noise R in the process to improve the accuracy and precision. The simulation results show that when the algorithm is applied in GPS/INS loosely coupled integrated system, the proposed method is capable of estimating the noise variance when the statistical characteristic is unknown or changed. The simulation also shows that the filtering results has a great improvement compared with other adaptive Kalman filter when using low-accuracy inertial sensors.

Key words: redundant measurement, difference sequence, noise characteristic, adaptive Kalman filter, integrated navigation system

CLC Number: