Version 1
: Received: 16 October 2018 / Approved: 16 October 2018 / Online: 16 October 2018 (08:49:55 CEST)
How to cite:
Fadhil, A.F.; Kanneganti, R.; Gupta, L.; Vaidyanathan, R. Fusion of Enhanced and Synthetic Vision System Images for Runway and Horizon Detection. Preprints2018, 2018100343. https://doi.org/10.20944/preprints201810.0343.v1
Fadhil, A.F.; Kanneganti, R.; Gupta, L.; Vaidyanathan, R. Fusion of Enhanced and Synthetic Vision System Images for Runway and Horizon Detection. Preprints 2018, 2018100343. https://doi.org/10.20944/preprints201810.0343.v1
Fadhil, A.F.; Kanneganti, R.; Gupta, L.; Vaidyanathan, R. Fusion of Enhanced and Synthetic Vision System Images for Runway and Horizon Detection. Preprints2018, 2018100343. https://doi.org/10.20944/preprints201810.0343.v1
APA Style
Fadhil, A.F., Kanneganti, R., Gupta, L., & Vaidyanathan, R. (2018). Fusion of Enhanced and Synthetic Vision System Images for Runway and Horizon Detection. Preprints. https://doi.org/10.20944/preprints201810.0343.v1
Chicago/Turabian Style
Fadhil, A.F., Lalit Gupta and Ravi Vaidyanathan. 2018 "Fusion of Enhanced and Synthetic Vision System Images for Runway and Horizon Detection" Preprints. https://doi.org/10.20944/preprints201810.0343.v1
Abstract
UAV network operation enables gathering and fusion from disparate information sources for flight control in both manned and unmanned platforms. In this investigation, a novel procedure for detecting runways and horizons as well as enhancing surrounding terrain is introduced based on fusion of enhanced vision system (EVS) and synthetic vision system (SVS) images. EVS and SVS image fusion has yet to be implemented real-world situations due to signal misalignment. We address this through a registration step to align the EVS and SVS images. Four fusion rules combining discrete wavelet transform (DWT) sub-bands are formulated, implemented and evaluated. The resulting procedure is tested on real EVS-SVS image pairs and pairs containing simulated turbulence. Evaluations reveal that runways and horizons can be detected accurately even in poor visibility. Furthermore, it is demonstrated that different aspects of the EVS and SVS images can be emphasized by using different DWT fusion rules. The procedure is autonomous throughout landing, irrespective of weather. We believe the fusion architecture developed holds promise for incorporation into head-up displays (HUDs) and UAV remote displays to assist pilots landing aircraft in poor lighting and varying weather. The algorithm also provided a basis rule selection in other signal fusion applications.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
