Machine-intelligent approach to automated gust-front detection for Doppler weather radars

Seth W. Troxel; Richard L. Delanoy

doi:10.1117/12.179602

13 July 1994 Machine intelligent approach to automated gust-front detection for Doppler weather radars

Seth W. Troxel, Richard L. Delanoy

Proceedings Volume 2220, Sensing, Imaging, and Vision for Control and Guidance of Aerospace Vehicles; (1994) https://doi.org/10.1117/12.179602
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

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.

Citation Download Citation

Seth W. Troxel and Richard L. Delanoy "Machine intelligent approach to automated gust-front detection for Doppler weather radars", Proc. SPIE 2220, Sensing, Imaging, and Vision for Control and Guidance of Aerospace Vehicles, (13 July 1994); https://doi.org/10.1117/12.179602

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