Approach to multisensor/multilook information fusion
Harley R. Myler, Ronald Patton · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1997
We are developing a multi-sensor, multi-look Artificial Intelligence Enhanced Information Processor (AIEIP) that combines classification elements of geometric hashing, neural networks and evolutionary algorithms in a synergistic combination. The fusion is coordinated using a piecewise level fusion algorithm that operates on probability data from statistics of the individual classifiers. Further, the AIEIP incorporates a knowledge-based system to aid a user in evaluating target data dynamically. The AIEIP is intended as a semi-autonomous system that not only fuses information from electronic data sources, but also has the capability to include human input derived from battlefield awareness and intelligence sources. The system would be useful in either advanced reconnaissance information fusion tasks where multiple fixed sensors and human observer inputs must be combined or for a dynamic fusion scenario incorporating an unmanned vehicle swarm with dynamic, multiple sensor data inputs. This paper represents our initial results from experiments and data analysis using the individual components of the AIEIP on FLIR target sets of ground vehicles.