A Novel State Estimation Method for a Land-vehicular Platform with Multirate Sampling

Fast attitude measurement is very important for a land-vehicular platform. However, the different sampling rates of the sensors will cause a bottleneck to the system response discrepancy. During the intersample period of the slow sensor, the self-propagation estimation error and estimated residual will deviate from the true value drastically by the model mismatch and inconsistent noise assumption. This paper proposes a novel state estimation method to deal with intersample compensation for a multirate attitude measurement fusion system. Referred to the lifting technique, the method introduces an adjustment factor matrix to minimize the difference between the estimated residual and the corresponding actual residual. The adjustment factor matrix will act as the Kalman gain with the estimated residual to compensate for the estimation error during the intersample period of the slow sensor. Simulations are conducted to evaluate the performance of the proposed method with several previous methods. The results show that the proposed multirate filter can improve the intersample compensation performance with a reasonable computational cost.