State estimation is critical for both process control and fault detection. A robust particle filter was proposed to estimate states in nonlinear process systems with uncertainty of initial states

which an indirect acceptance criterion was introduced to determine accuracy of the initial states and then to decide the needs for iterative improvement on the initial states by the feedback mechanism of observation bias. In case that the initial states were inaccurate

the iterative improvement strategy would be triggered to adjust particles closer to the true initial states. Therefore

the probability of setting the correct initial states to particles was increased and the accuracy of state estimation was improved through particle filter iteration. When applied to two nonlinear dynamic systems

the proposed particle filter demonstrated much more effectiveness and robustness than the traditional particle filter.