Improved unscented particle filter
改进的无迹粒子滤波算法

Being different to the unscented particle filter(UPF) in which each particle represents a sample of the statesequence, the improved unscented particle filter(IUPF) has its particle representing a sample of the extended processnoise-sequence which is the combination of the initial states and the process-noise-sequence. For the different form of the state-space, a correspondent unscented transformation(UT) method is adopted to construct the proposal distribution. This method draws ideas from the unscented-transformation-based-auxiliary-particle-filter(UTAPF) to improve the re-sampling process. The IUPF has three advantages over the UPF and the UTAPF. Firstly, the IUPF requires no knowledge of the state transition kernel; thus, it has a wider application scope. Secondly, the IUPF has a lower computational cost. Thirdly, each particle in the UPF or the UTAPF is generally assumed to have a state distribution inherited from its parent particles, but the reason is questionable. However, this assumption can be avoided in the IUPF. In two simulation experiments of ours, the IUPF shows better estimation performance than the other four algorithms. Compared with the UPF and the UTAPF, the IUPF reduces the computation time by an amount depending on the dimension of the state vector and the dimension of the noise vector.