MEMS INS/GPS Data Fusion using Particle Filter
AbstractCoupling GPS with Micro-Electro-Mechanical Systems (MEMS) Inertial Navigation Systems (INS) is an challenging way of improving land vehicle navigation performance. MEMS inertial sensors suffer from complex stochastic errors, which are difficult to compensate and model using conventional Kalman Filter, as it provides an effective solution to the linear Gaussian filtering problem. However where there is nonlinearity, either in the model specification or observation process, other method are required. Particle filtering techniques are good candidates to solve the corresponding nonlinear estimation problem associated to MEMS INS/GPS hybridization. Here nonlinear models for accelerometer were successfully implemented using particle filter (PF) . The performance of the resulting algorithm was illustrated through experimental results. Also the position estimation results using PF during GPS signal outages were presented. Ill. 4, bibl. 5 (in English; abstracts in English and Lithuanian).
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