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Identification of Abnormal System Noise Temperature Patterns in Deep Space Network Antennas Using Neural Network Trained Fuzzy LogicThis paper presents the development of a fuzzy logic function trained by an artificial neural network to classify the system noise temperature (SNT) of antennas in the NASA Deep Space Network (DSN). The SNT data were classified into normal, marginal, and abnormal classes. The irregular SNT pattern was further correlated with link margin and weather data. A reasonably good correlation is detected among high SNT, low link margin and the effect of bad weather; however we also saw some unexpected non-correlations which merit further study in the future.
Document ID
20120009693
Document Type
Conference Paper
External Source(s)
Authors
Lu, Thomas (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Pham, Timothy (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Liao, Jason (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
August 25, 2013
Publication Date
April 17, 2011
Subject Category
Space Communications, Spacecraft Communications, Command and Tracking
Meeting Information
3rd International Conference on Advances in Satellite and Space Communication(Budapest)
Distribution Limits
Public
Copyright
Other
Keywords
pattern identification
fuzzy logic
Deep Space Network (DSN)
neural network training
system noise temperature