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Proceedings Paper

Machine-intelligent approach to automated gust-front detection for Doppler weather radars
Author(s): Seth W. Troxel; Richard L. Delanoy
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Paper Abstract

Automated gust front detection is an important component of the airport surveillance radar with wind shear processor (ASR-9 WSP) and terminal Doppler weather radar (TDWR) systems being developed for airport terminal areas. Gust fronts produce signatures in Doppler radar imagery which are often weak, ambiguous, or conditional, making detection and continuous tracking of gust fronts challenging. A machine intelligent gust front algorithm (MIGFA) has been developed that makes use of two new techniques of knowledge-based signal processing: functional template correlation (FTC), a generalized matched filter incorporating aspects of fuzzy set theory; and the use of `interest' as a medium for pixel-level data fusion. This paper focuses on the more recently developed TDWR MIGFA, describing the signal-processing techniques used and general algorithm design. A quantitative performance analysis using data collected during recent real-time testing of the TDWR MIGFA in Orlando, Florida is also presented. Results show that MIGFA substantially outperforms the gust front detection algorithm used in current TDWR systems.

Paper Details

Date Published: 13 July 1994
PDF: 12 pages
Proc. SPIE 2220, Sensing, Imaging, and Vision for Control and Guidance of Aerospace Vehicles, (13 July 1994); doi: 10.1117/12.179602
Show Author Affiliations
Seth W. Troxel, MIT Lincoln Lab. (United States)
Richard L. Delanoy, MIT Lincoln Lab. (United States)

Published in SPIE Proceedings Vol. 2220:
Sensing, Imaging, and Vision for Control and Guidance of Aerospace Vehicles
Jacques G. Verly; Sharon S. Welch, Editor(s)

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