一种基于冗余测量的自适应卡尔曼滤波算法

doi:10.7527/S1000-6893.2015.0001

Abstract

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:

ZHOU Qifan, ZHANG Hai, WANG Yanran. A redundant measurement adaptive Kalman filter algorithm[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2015, 36(5): 1596-1605.

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A redundant measurement adaptive Kalman filter algorithm

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