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A data fusion algorithm for multi-sensor microburst hazard assessmentA recursive model-based data fusion algorithm for multi-sensor microburst hazard assessment is described. An analytical microburst model is used to approximate the actual windfield, and a set of 'best' model parameters are estimated from measured winds. The winds corresponding to the best parameter set can then be used to compute alerting factors such as microburst position, extent, and intensity. The estimation algorithm is based on an iterated extended Kalman filter which uses the microburst model parameters as state variables. Microburst state dynamic and process noise parameters are chosen based on measured microburst statistics. The estimation method is applied to data from a time-varying computational simulation of a historical microburst event to demonstrate its capabilities and limitations. Selection of filter parameters and initial conditions is discussed. Computational requirements and datalink bandwidth considerations are also addressed.
Document ID
19940022784
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Wanke, Craig R. (Massachusetts Inst. of Tech. Cambridge, MA, United States)
Hansman, R. John (Massachusetts Inst. of Tech. Cambridge, MA, United States)
Date Acquired
September 6, 2013
Publication Date
February 1, 1994
Publication Information
Publication: NASA. Langley Research Center, FAA(NASA Joint University Program for Air Transportation Research, 1992-1993
Subject Category
Air Transportation And Safety
Accession Number
94N27287
Funding Number(s)
CONTRACT_GRANT: F19628-90-C-0002
CONTRACT_GRANT: NGL-22-009-640
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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