Multisensor fusion and unknown statistics
R. Joshi, Arthur C. Sanderson · 2002
Traditional data interpretation methods do not prescribe a well-defined methodology for sensors which are difficult to characterize by well-defined statistical uncertainty models. We describe a minimal representation approach that characterizes the "information" in observed data by its coding complexity; this "information" is well-defined for sensors with known and unknown statistics. For statistical data, this information-based approach subsumes classical approaches, while for sensors with unknown statistics it provides a new paradigm for uncertainty modeling based on accuracy and precision (AP), and allows fusion with statistical data. An abstract multisensor data interpretation problem is described and formulated using the minimal representation approach. Monte-Carlo simulations comparing the use of an AP-coding uncertainty model with a Gaussian uncertainty model for a two-dimensional pose estimation problem are presented.