Ranging performance is described for photoelectric equipment reconnaissance using an active laser detection system that is based on the 'cat's eyes' effect of optical windows. Active laser detection systems have an advantage over passive systems because they can measure target velocity and spatial coordinates. However, there are several challenging problems here because of the great distances involved, the low returned power of the uncooperative target, and the optical aberrations induced by the atmosphere. In the design of this system, the principle of detection is based on the 'cat's eyes' effect according to which the optical windows of photoelectric equipments have a strong reflect character towards incident laser beam. With 'cat's eyes' effect, the detection of uncooperative target can be translated into one of a cooperative target, so the ratio of returned laser can be increased. In this paper, the ranging performance presented here takes into account all the various elements of the system, from the laser emission, target, atmospheric propagation to the detector. The characteristics of back-reflected laser and an estimate of the laser Cross Section (LCS) from 'cat's eyes target' are investigated in theory and simulation. The Signal-to-Noise Ratio (SNR) is calculated by combining the probability of detection of the system for given electronic characteristics of the system and for a given probability of false alarms. On the basis of analysis of SNR, minimum detectable signal power, operating distance of the system and factors affecting the ranging performance is analyzed. Results indicate that system has characters of long range, and high sensitivity. It can be used to detect the aerial targets such as reconnaissance drone, navigate missile, reconnaissance satellite etc.

Date Published: 10 June 2006
PDF: 7 pages
Proc. SPIE 6344, Advanced Laser Technologies 2005, 63442P (10 June 2006); doi: 10.1117/12.694133

Published in SPIE Proceedings Vol. 6344:
Advanced Laser Technologies 2005
Ivan A. Shcherbakov; Kexin Xu; Qingyue Wang; Alexander V. Priezzhev; Vladimir I. Pustovoy, Editor(s)