Introduction - If you have any usage issues, please Google them yourself
The title of this article is to improve the particle filter in the navigation of the applied research. CAJ can be used to open the document. This issue initially on the GPS/DR Vehicle Location System portfolio model, and then the analysis of nonlinear filtering based on the introduction of a particle filter. Particle filter is a recursive calculation based on Sequential Monte Carlo algorithm, it uses a set of probability density function from random samples and weights attached to the relevant set of particles to approximate a posteriori probability density, and thus not subject to non-linear, the issue of non-Gaussian constraints. Although there are many advantages of particle filter, yet it still exists, such as particle number Punic poor, filter performance is not high, real-time poor.