Abstract
High demands on accuracy and reliability of real-time navigation and positioning applications require the exploitation of multiple sensors. However, in many cases, the relations between the state vector and the observation space are of nonlinear mathematical nature or observations have stochastic properties which deviate from those of Gaussian normal distributions. Thus, the extended Kalman filter and its variants are not always the most suitable choice. For such problems a particle filter, or more generally, sequential Monte Carlo methods, can increase the reliability of the estimates. Since particle filters are wellsuited for parallel data processing, it will be shown how single point positioning GNSS solutions can be obtained when using a graphics processing unit (GPU) as a massive parallel computing device. The implementation of this approach and evaluation of its performance concerning real-time capability will be discussed as well as its precision and accuracy compared to a standard Kalman filter solution.
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