IMM fusion estimation with multiple asynchronous sensors

This paper presents an asynchronous IMM fusion estimation algorithm for stochastic multi-model systems with multiple asynchronous sensors. Sampling rates of the sensors we considered are arbitrary as well as initial sampling time instants. Asynchronous measurements collected in each filtering interval are sorted in time sequence, and transformed to the fusion time instant as an equivalent measurement. Then, the equivalent measurement is used to update elemental filters in IMM, taking into account the correlation between the equivalent measurement noise and the process noise. Model transitions at asynchronous sampling time instants in the fusion interval are considered. Elemental filters are both re-initialized and updated conditioned on the model transition sequence in the fusion filtering interval. The fused estimate and covariance are obtained by combining model-sequence conditioned estimates and covariances with probabilities of corresponding model sequences. In addition, an equivalent recursive form is derived to reduce the computational complexity of the proposed algorithm. The proposed algorithm avoids the counter-intuitive performance degradation phenomenon of the sequential IMM filtering approach. Finally, simulation results are given to illustrate the feasibility and effectiveness of the proposed algorithm.