IJE TRANSACTIONS A: Basics Vol. 29, No. 4 (April 2016) 500-504   

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Z. Liwen, S. Ming, L. Na, Z. Qipeng and C. Xiangzheng
( Received: February 15, 2016 – Accepted in Revised Form: April 14, 2016 )

Abstract    The detection probability of visible light reconnaissance equipment is one of key indexes to assess the performance of the system. The detection probability is determined by many factors, such as atmospheric visibility, target-background contrast, target size and distance, solar elevation angle etc. Based on the detection probability model of the visible light reconnaissance equipment to gain targets, chief factors affecting the reconnaissance capability of the visible light reconnaissance equipment are analyzed. With the simulation calculation of the model, the relations between the detection probability model of the visible light reconnaissance equipment and the parameters, which including the contrast between the target and background, atmospheric visibility, the solar elevation angles, the optical system magnification and intercept range, are analyzed. All these are beneficial to further evaluate the reconnaissance capabilities of the visible light reconnaissance equipment.


Keywords    Visible light reconnaissance equipment, Detection probability, Intercept range, Target-background contrast


چکیده    احتمال تشخیص تجهیزات شناسایی نور مرئی یکی از شاخص های کلیدی برای ارزیابی عملکرد سیستم است. احتمال تشخیص توسط عوامل بسیاری مثل دید اتمسفر، کنتراست هدف- پس زمینه، اندازه هدف و فاصله، زاویه ارتفاع خورشیدی و غیره تعیین می شود. بر پایه مدل احتمال تشخیص برای تجهیزات شناسایی نور مرئی و به دست آوردن اهداف، عوامل مهم تاثیرگذار بر قابلیت شناسایی تجهیزات شناسایی نور مرئی مورد تحلیل قرار گرفت. با محاسبات شبیه سازی مدل، روابط بین مدل احتمال تشخیص برای تجهیزات شناسایی نور مرئی و پارامترها، شامل کنتراست بین هدف و پس زمینه، دید اتمسفر، زوایای ارتفاع خورشیدی، بزرگنمایی سیستم نوری و طیف وسیعی رهگیری، تجزیه و تحلیل شد. همه این ها برای بررسی بیشتر قابلیت شناسایی تجهیزات شناسایی نور مرئی مفید است.


1.     Han, W.H., Bai, Z.P. and Wang, J.Y., "Exploration of calculation methods for target detection probability", Electro-optic Technology Application,  Vol. 22, No. 2, (2007), 25-27.

2.     Kouba, J.T. and Bose, S.C., "Terminal seeker pointing-angle error at target acquisition", Aerospace and Electronic Systems, IEEE Transactions on,  Vol., No. 3, (1980), 313-319.

3.     Han, G.X., He, J. and Qi, J.Q., "Fused detection probability model of nrcs based on rank k criterion", Journal of Naval University of Engineering,  Vol. 26, No. 1, (2014), 64-70.

4.     Feizi, A., Aghagolzadeh, A. and Seyedarabi, H., "Using combined local object based features and cluster fusion for the behaviors recognition and detection of abnormal behaviors", International Journal of Engineering Vol. 28, No. 11, (2015), 1597-1604.

5.     Mardia, H., "New techniques for the deinterleaving of repetitive sequences", in Radar and Signal Processing, IEE Proceedings F, IET. Vol. 136, (1989), 149-154.

6.     Milojevic, D. and Popović, B., "Improved algorithm for the deinterleaving of radar pulses", in Radar and Signal Processing, IEE Proceedings F, IET. Vol. 139, (1992), 98-104.

7.     Jain, M., Agrawal, S. and Preeti, C., "Fuzzy reliability evaluation of a repairable system with imperfect coverage, reboot and common-cause shock failure", International Journal of Engineering,  Vol. 25, No. 3, (2012), 231-238.

8.     Nishiguchi, K.I. and Kobayashi, M., "Improved algorithm for estimating pulse repetition intervals", Aerospace and Electronic Systems, IEEE Transactions on,  Vol. 36, No. 2, (2000), 407-421.

9.     FAN, W., WANG, Y. and RAO, R.-z., "Wavelength band selection method for taget detection based on character of atmosphere radiation [j]", Infrared and Laser Engineering,  Vol. 2, (2005), 177-189.

10.   Khakpour, F. and Ardeshir, G., "Using a novel concept of potential pixel energy for object tracking", International Journal of Engineering-Transactions A: Basics,  Vol. 27, No. 7, (2013), 1023-1032.

11.   DENG, J., GAO, F. and ZHANG, L., "Equivalent test of optical camouflage effectiveness by reducing observation distance", Journal of Detection & Control,  Vol. 3, (2012), 19-22.

12.   XU, H., ZHANG, J.-j., YUAN, Y.-h., ZHANG, P.-h. and HAN, B., "Detection probability of infrared and visible image fusion system", Optics and Precision Engineering,  Vol. 12, (2013), 3205-3213.

13.   LI, S.-g., NIE, J.-s. and LI, H., "Effectiveness analysis on eyeballing optical reconnaissance under a typical atmospheric condition [j]", Infrared and Laser Engineering,  Vol. 5, No., (2010), 915-919.

14.   Toloei, A. and Niazi, S., "Estimation of los rates for target tracking problems using ekf and ukf algorithms-a comparative study", International Journal of Engineering-Transactions B: Applications,  Vol. 28, No. 2, (2014), 172-179.

15.   Yi, W., Wei, F. and Ruizhong, R., "The analysis of the target—background contrast undera typical atmospheric condition", Laser&Infrared,  Vol. 34, No. 5, (2004), 467-471.

16.   Liang, D.M., Zhang, M.J. and Zhang, Y.X., "Equivalent calculation anaiysis for operating range of visible light reconnaissance system", Modern Electronics Technique,  Vol. 36, No. 1, (2013), 25-27.

17.   Bailey, H., Target detection through visual recognition: A quantitative model. 1970, DTIC Document.

18.   TANG, H. and JIN, W., "Testing conditions of visibility distance and their effects on results [j]", OPTICAL TECHNOLOGY,  Vol. 1, (1999), 80-84.

19.   Xie, Q.-F., "Ground-target model of radar detection based on stochastic theory", Huoli yu Zhihui Kongzhi,  Vol. 36, No. 10, (2011), 131-133.

20.   Huschke, R., "Atmospheric visual and infrared transmission deduced from surface weather observations, Rand,  (1976), 9-12.

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