To resolve the weight degeneracy in condensation algorithm which uses transition prior as the proposal
distribution, a unscented particle filter algorithm(ISUKF-PF) is proposed by using iterated minimal skew simplex
UKF(ISUKF) as the proposal distribution. Statistical liner error propagations are obtained by ISUKF; and the IEKF iterated
equations are derived by replacing the system model Jacobian matrix with statistical liner error propagation terms. Then
the states mean and covariance are iterated and updated by the IEKF iterated equations to be convergent to the state MAP
estimation for near zero-residual. The outputs of the ISUKF-PF have the higher estimation accuracy, smoothing errors by
one-step prediction of states estimations. The results show that the ISUKF achieves the more overlap regions of prediction
samples and peak zones of observation likelihood and increases the accuracy of state estimating in nonlinear system.