Sequential Monte Carlo Filters and Integrated Navigation
Per-Johan Nordlund · 2002
In this thesis we consider recursive Bayesian estimation in general, and sequential Monte Carlo filters in particular, applied to integrated navigation. Based on a large number of simulations of the model, the sequential Monte Carlo lter, also referred to as particle filter, provides an empirical estimate of the full posterior probability density of the system. The particle filter provide a solution to the general non-linear, non-Gaussian ltering problem. The more nonlinear system, or the more non-Gaussian noise, the more potential particle filters have. Although very promising even for high-dimensional systems, sequential Monte Carlo methods suer from being more or less computer intensive. However, many systems can be divided into two parts, where the first part is nonlinear and the second is (almost) linear conditionally upon the first. By applying the particle filter only on the severly nonlinear part of lower dimension, the computational load can be significantly reduced. For the remaining conditionally (almost) linear part we apply (linearized) linear filters, such as the (extended) Kalman filter. From a