Study On Particle Filter With Application In SINS

China’s coal resources have a poor production status，once when unexpected coalmine accidents occur，the existing underground personnel positioning system couldnot be competent to provide the accurate distribution and running track of miners andcoal mine equipment, resulting in the best time for rescuing missed. In view of this,it’s necessary to complete the underground personnel positioning system to improvepositioning accuracy, strapdown inertial navigation system (SINS) could deal betterwith this problem.As a crucial technology of Inertial Navigation System, the alignment accuracyhas a far-reaching impact on navigation accuracy. When misalignment angle is smalland Gaussian noises, Kalman filter results in a good estimate; While when it sufferslarge misalignment and Non-Gaussian noises, the existing linear filter algorithms areno longer applicable, for this case，nonlinear filtering algorithm particle filter isproposed to achieve the alignment precision in this paper.The dissertation investigates the particle filter algorithm deeply and underlyingflaws are discussed. For particle degradation, Spherical Simplex sampling method hasbeen applied for the reconstruction of the important function; for particle depletion,MH sampling algorithm also has been used to improve particle diversity whenresampling, and the algorithms are analyzed after simulation, the improved particlefilter algorithm meets the goal.In view of the coal miners and equipment’s movement pattern is relativelysimple and not high mobility, reasonable alignment models of large misalignmenterror in mine have been deduced; then the improved particle filter algorithm issimulated in SINS alignment, the simulations confirm the feasibility and rationality ofnon-liner algorithm, and disinctions are analyzed in terms of precision and time aboutSSUPF-MH、SSUPF、UPF.