Modified Gaussian Particle Filtering Method in Aperiodic Sparseness Sampling Environment

The study of the particle filter (PF) has been the hot and difficult problems in the field of nonlinear filtering. To the state estimate of target in Aperiodic Sparseness Sampling Environment, a modified Gaussian particle filtering method based on target motion characteristic is proposed. Firstly, the reasons of the traditional particle filter that can not effectively deal with Aperiodic Sparseness Sampling measurements were analyzed. Secondly, in order to improve the estimated accuracy of the predicted particles and covariance of Gaussian particle filter, the proposed algorithm incorporate target observation, time interval of the target observation and the target speed into the construction of important density function of Gaussian particle filter. Finally, the experimental results show that the performance of the proposed algorithm is slightly better than these of the traditional PF, auxiliary particle filtering(APF) and Gaussian particle filter (GPF) for one-dimensional nonlinear non-Gaussian; but it also shows that the performance of the proposed algorithm is much better than these of the PF ，APF and GPF for aperiodic sparseness sampling environment.