GPS NAVIGATION USING INTERACTING MULTIPLE MODEL (IMM) ESTIMATOR AND PROBABILISTIC DATA ASSOCIATION FILTER (PDAF)

摘要

A method for GPS navigation which uses an interacting multiple-model (IMM) estimator with a probabilistic data association filter (PDAF) improves navigation performance. The method includes (a) providing two or more models of GPS navigation, with each model characterized by a model state vector which is updated periodically, (b) providing for each model a corresponding filter for deriving, for each period, a current value for the corresponding model state vector based on current measurements made on parameters affecting the corresponding state vector; and (c) applying an interacting multiple model (IMM) estimator to provide, for each period, a current value for a system state vector using the current values of the model state vectors for that period and their corresponding filters. Each model state vector may include one or more of the following: variables: 3-dimensional position, 3-dimensional velocity, satellite clock bias, satellite clock drifts and one or more other satellite parameters. The current value of the system state vector may be a weighted average of the current values of the model state vectors, where the weights are a set of mode probabilities. In addition, one or more of the filters is a probabilitic data association filter (PDAF).