Model-based multisensor data fusion: a minimal representation approach

A general approach to model-based multisensor data fusion using a minimal representation size criterion is described. Each sensor is modeled by a general constraint equation which defines a data constraint manifold (DCM), and observed sensor data populate the measurement space according to these constraints. The choice of multisensor interpretations is based on a minimal representation size criterion which evaluates the complexity through correspondence and encoded errors weighted by relative sensor accuracy and precision. This general framework automatically selects subsets of data features called constraining data feature sets (CDFS) and chooses the CDFS corresponding to a minimal representation interpretation of the observed data. The resulting procedure fuses heterogeneous sensor readings into a single estimation method. The method is illustrated for a visual and tactile data fusion example. The approach generalizes to problems with non-geometric models, and can be used for multisensor system identification in other domains such as process control.<<ETX>>