Evolving neural networks for video attitude and height sensor

Zhixiong Zhang; Kenneth J. Hintz

doi:10.1117/12.213031

5 July 1995 Evolving neural networks for video attitude and height sensor

Zhixiong Zhang, Kenneth J. Hintz

Author Affiliations +

Proceedings Volume 2484, Signal Processing, Sensor Fusion, and Target Recognition IV; (1995) https://doi.org/10.1117/12.213031
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

The development of an on-board video attitude and height sensor (VAHS) which is used to measure the height, roll, and pitch of an airborne vehicle at low altitude, is presented. The VAHS consists of a downlooking TV camera and two orthogonal sets of laser diodes (total four laser diodes) producing a structured light pattern. Although the height, roll, and pitch can be determined by measuring the locations of the dots in the image, in practice it is very difficult to precisely align the laser diodes and the TV camera. Moreover it is also very hard to obtain accurate camera parameters, because of its various nonlinear distortions. An approach which uses layered neural networks (NNs) to map the locations of the dots in the image to the height, roll, and pitch of the airborne vehicle, is presented here. Amorphous NNs have also been evolved by genetic algorithms (GA) with mixed results. Some simulation results of these experiments are presented.

Citation Download Citation

Zhixiong Zhang and Kenneth J. Hintz "Evolving neural networks for video attitude and height sensor", Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); https://doi.org/10.1117/12.213031

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE