Particle Filter Algorithm And Its Application In Inertial Navigation Systems

As a crucial technology of INS (Inertia Navigation System), the initial alignment accuracy concerns the working performance of INS directly. The Kalman filtering is a primary method in initial alignment, which requires small misalignment angles and Gaussian noises. However, the INS always works in rugged environment. Especially to SINS (Strap-down Inertia Navigation System), which often suffer interferences from various kinds of factors, the initial heading error is relatively large. The linear alignment model based on small misalignment angles cannot describe the system error characteristic exactly, and the Kalman filtering always fails in this situation. So, it is necessary to study the nonlinear filtering algorithm and nonlinear alignment technology under the large heading error.In order to study the nonlinear alignment technology of SINS, a nonlinear filtering algorithm, which is called particle filter, is discussed firstly in this thesis. The fundamental principle and committed steps are introduced in detail, and the underlying flaws are pointed out, which include degeneration, sample impoverishment and the high computing complexity. Then we adopt many ways to improve the particle filter. We use traditional Gaussian approximations to produce the better importance function so as to decrease the phenomenon of degeneration. We adopt some intelligent optimization approaches and propose two algorithms which is called PSOPF and APF to guarantee the sample set diversity so as to overcome the phenomenon of sample impoverishment. We combine traditional nonlinear filtering algorithm with particle filter and form mixed filtering approach to decrease the computing complexity rapidly without reducing the accuracy of estimation.After that, by deriving the initial alignment errors equation of SINS and considering the gyro stochastic drift and the accelerometer stochastic bias, this thesis gives the nonlinear alignment model on the condition of the heading error being large. Finally, we applied standard Kalman filtering, generic particle filtering algorithm and the improved particle filtering algorithm to the simulation of nonlinear alignment above-mentioned. From the comparison with each other in several ways, it is clear that these improved particle filtering algorithms have better performances than...