You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
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.
The alert did not successfully save. Please try again later.
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