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Abstract
An adaptive learning fusion processor, capable of fusion of a mix of information at the data, feature, and decision levels, acquired from multiple sources (sensors as well as feature extractors and/or decision processors) is presented. Four alternative approaches: a self- partitioning neural net, an adaptive fusion process, an evidential reasoning approach, and a concurrence seeking approach were initially evaluated from a conceptual viewpoint followed by some limited simulation and testing. Based on this assessment, an adaptive fusion processor employing innovative advances of the nearest neighbor concept was selected for detailed implementation and testing using real-world field data. Results show the benefits of fusion in terms of improved performance as compared to those obtainable from the individual component information streams being input to the fusion processor and clearly bring out the feasibility and effectiveness of the new multi-level fusion concepts.
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Belur V. Dasarathy "Adaptive fusion processor", Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); https://doi.org/10.1117/12.213007
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