Share Email Print

Proceedings Paper

A mmW image-based algorithm on wire recognition for DVE applications
Author(s): Darren S. Goshi; Ming-Ting Sun; John Kirk Jr.
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

In this work, we present the framework surrounding the development of a mmW radar image-based algorithm for wire recognition and classification for rotorcraft operation in degraded visual environments. While a mmW sensor image lacks the optical resolution and perspective of an IR or LIDAR sensor, it currently presents the only true see-through mitigation under the heaviest of degraded vision conditions. Additionally, the mmW sensor produces a high-resolution, radar map that has proven to be exceedingly interpretable, especially to a familiar operator. Seizing on these clear advantages, the mmW radar image-based algorithm is trained and evaluated against independent mmW imagery data collected from a live flight test in a relevant environment. The foundation of our approach is based on image processing and machine learning techniques utilizing radar-based signal properties and sensor and platform information for added robustness. We discuss some of the requirements and practical challenges of a standalone algorithm development, and lastly, present some preliminary examples using existing development tools and discuss the path for continued advancement and evaluation.

Paper Details

Date Published: 5 June 2017
PDF: 6 pages
Proc. SPIE 10197, Degraded Environments: Sensing, Processing, and Display 2017, 101970C (5 June 2017); doi: 10.1117/12.2262250
Show Author Affiliations
Darren S. Goshi, Honeywell International Inc. (United States)
Ming-Ting Sun, Univ. of Washington (United States)
John Kirk Jr., Goleta Star, LLC (United States)

Published in SPIE Proceedings Vol. 10197:
Degraded Environments: Sensing, Processing, and Display 2017
John (Jack) N. Sanders-Reed; Jarvis (Trey) J. Arthur III, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?