Multi-sensor data fusion for situational assessment-a critical element of systems integration, some theory and application to collision avoidance

摘要

Concerns multisensor data fusion (MSDF) for situational assessment for real time complex processes. All three elements of the SHORE (stimulus-hypothesis-response) paradigm have been considered by the ISIS group for demonstrating this architecture for systems integration of a fully autonomous road, cross-country and drilling vehicle on CEC Project Panorama. MSDF is a continuous process dealing with the association correlation, and combination of data and information from multiple disparate sources to achieve a refined state estimate about the environment and timely assessment of the situation. Here we only consider the processes of data integration and state estimation. To integrate data from disparate data sources such as sensors, look-up tables, human experiences/observations, data bases, etc a common currency of information content and data representation is required. Existing theories such as Bayesian, Dempster-Shafer, artificial neural networks (ANN), case-based reasoning, method of endorsement, blackboard expert systems, fuzzy logic etc.-all of which have been used for MSDF-are inadequate or inappropriate. We propose neurofuzzy algorithms, since they readily incorporate database knowledge/symbolic/linguistic knowledge in the form of fuzzy rules, and sensory data in a single environment/processor.