A Horizontal Model Fusion Paradigm.
Presented at: UNSPECIFIED.
In navigation and tracking problems, the identification of an appropriate model of vehicular or target motion is vital to most practical data fusion algorithms. The true system dynamics are rarely known, and approximations are usually employed. Since systems can exhibit strikingly different behaviors, multiple models may be needed to describe each of these behaviors. Current methods either use model switching (a single process model is chosen from the set using a decision rule) or consider the models as a set of competing hypothesis, only one of which is 'correct'. However, these methods fail to exploit the fact that all models are of the same system and that all of them are, to some degree, 'correct'. In this paper we present a new paradigm for fusing information from a set of multiple process models. The predictions from each process model are regarded as observations which are corrupted by correlated noise. By employing the standard Kalman filter equations we combine data from multiple sensors and multiple process models optimally. There are a number of significant practical advantages to this technique. First, the performance of the system always equals or betters that of the best estimator in the set of models being used. Second, the same decision theoretic machinery can be used to select the process models as well as the sensor suites.
|Type:||Conference item (UNSPECIFIED)|
|Title:||A Horizontal Model Fusion Paradigm|
|Additional information:||Title of conference: Navigation and Control Technologies for Unmanned Systems|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
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