In this study, we proposed an enhanced particle filter based on PSSB, which involves dividing the target and input value

In this study, we proposed an enhanced particle filter based on PSSB, which involves segmenting the target and input values into multiple segments. We subsequently established a piecewise matching mechanism based on the piecewise tendency and selected the most suitable function. The mathematical model of the fitting function approximating the target value was obtained through piecewise fitting. This model was then utilized as the input for the particle filter, enabling piecewise particle filtering. Our comparative experimental results demonstrate that the PSBB-PF approach significantly improves precision by 29.67%, 55.33%, 61.33%, 60.44%, and 61.22% compared to PF, EPF, IPE, KF-PF, and observed value, respectively, in the nonlinear system. In the linear system, PSBB-PF also exhibits optimal prediction performance. These findings provide compelling evidence supporting the effectiveness of our proposed method.