Signal Processing For Hybridization
SATELLITE AND TERRESTRIAL RADIO POSITIONING TECHNIQUES: A SIGNAL PROCESSING PERSPECTIVE(2012)
摘要
Bayesian filters are a good statistical tool to jointly combine information data measured by heterogeneous sensors in order to increase the accuracy of the positioning system. Recursive Bayesian estimation is a general probabilistic approach for determining an unknown probability density function recursively over time using incoming measurements and a mathematical process model. Higher positioning accuracy can be achieved by means of hybrid techniques which take advantage of smart combination of different types of measurements based on different technologies. When a KF is used, usually a sensitivity analysis is performed in order to find the numerical parameters that best fit the model to the actual target trajectories. Such analysis is strongly case-dependent since these parameters depend on factors that change in different scenarios such as target dynamics, sampling frequency, and noise variance. Particle filters realize Bayes filter updates according to a sampling procedure, often referred to as sequential importance sampling. The key advantage of particle filters is their ability to represent arbitrary probability densities.
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关键词
hybridization,particle filter,inertial navigation systems,kalman filter
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