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.