Multi-sensor adaptive particle filter in measurement uncertainty

Aiming at the effective utilization of multi-sensor measurement in measurement uncertainty, a multi-sensor adaptive particle filter algorithm is proposed. In the algorithm, multi-sensor measurement set is sampled by the random sampling strategy and measurement model transition probability. Then state estimation and the update of multi-sensor measurement set are realized by re-sampling in particle filter. Finally, the current moment measurement is validated according to the proportion of measurement number of single sensor in multi-sensor measurement set after re-sampling. The adverse influence of interference to the computational complexity is by reasonably selecting effective measurement. The theoretical analysis and experimental results show the efficiency of the proposed algorithm.