Incremental particle filter method

An incremental particle filter(IPF)model and analysis method were put forward, while the concept and recursive calculative steps were established. For the measurement data with unknown system errors in practical engineering, accurate measurement model cannot be established. The presented IPF method established accurate measurement likelihood function by calibrating the unknown system errors in the measurement data, and accurate incremental particle filter model was obtained. This can eliminate these unknown system errors, decrease the number of resampling, effectively preserve the diversity of particles and improve the accuracy of nonlinear filtering. In simulation, the number of resampling decreased by 41.7 percentages, and filtering error mean value and RMSE (root mean square error) decreased by 45.3 percentages and 70.1 percentages, respectively, improving the performance of filtering effectively.