Algorithm for determining the probability of target detection and recognition by an aviation thermal vision system limited by noise
Heading:
| 1Kolobrodov, VG, 2Lykholit, MI, 2Tiagur, VM, 1Vasylkovska, IO 1National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine 2Special Device Production State Enterprise “Arsenal”, Kyiv, Ukraine |
| Space Sci. & Technol. 2024, 30 ;(3):22-30 |
| https://doi.org/10.15407/knit2024.03.022 |
| Publication Language: Ukrainian |
Abstract: Thermal imaging equipment is widely used in modern military operations due to its ability to observe targets even in adverse weather conditions continuously. Thermal imaging surveillance systems (TISS) are primarily designed to detect, recognize, and identify targets. Despite the progress, existing methods do not allow quickly calculating the probability of detecting, recognizing, or identifying a target at user-defined distances from the TISS to the target.
The article aims to develop a more advanced method and algorithm for calculating the probability of detection, recognition, and identification of a target by a noise-limited thermal imaging surveillance system.
A more advanced (convenient) algorithm and method for calculating the probability of detection, recognition, and identification of an object (target) by a thermal imaging surveillance system at a given range have been developed. This is based on the minimum resolution temperature difference, Johnson's criterion, as well as the TTPF and TRTPF functions. The proposed algorithm makes it relatively easy to calculate the probability of detecting, recognizing, and identifying a target by a thermal imaging system, which is limited by system noise.
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| Keywords: aviation thermal imaging system, probability of detecting, probability transfer function based on target distance, recognizing and identifying the target, target range, threshold contrast perception |
References:
1. Definition of nominal static ranger performance for thermal imaging systems (1995). STANAG 4347.
2. Driggers R. G., Friedman M. H., Devitt J. W., Furxhi O., Singh A. (2022). Introduction to infrared and electro-optical systems (3th ed.). Artech House, 712 p.
3. Kaplan H. (2010). Practical applications of infrared thermal sensing and imaging equipment (3th ed.). Washington, USA: SPIE Press, 192 p.
4. Keßler S., Galb R., Wittenstein W. (2017). TRM4: Range performance model for electro-optical imaging Systems. Fraunhofer Institute of Optronics, System Technologies and Image. Proc. of SPIE, 10178, 101780P.
https://doi.org/10.1117/12.2262543
5. Kolobrodov V. H., Lykholit M. I. (2007). Design of thermal imaging and television surveillance systems. Kyiv: NTUU KPI, 364 p.
6. Kolobrodov V. H., Lykholit M. I., Tiahur V. M., Vasylkovska I.O., Kolobrodov M. S. (2023). Probability of target discrimination by the contrast-limited thermal vision system of a UAV. Space Science and Technology, 29(5), 20-32.
https://doi.org/10.15407/knit2023.05.020
7. Lloyd J. M. (1975). Thermal Imaging Systems Optical physics and engineering. Plenum Press, 456 p.
8. Melamed R., Yitzhaky Y., Kopeika N. S., Rotman S. R. (1998). Experimental comparison of three target acquisition models. Opt. Engineering, 37(7), 1902-1913.
https://doi.org/10.1117/1.602029