Detectability of Low Characteristic Aircraft Based on Different Ground-Based Infrared Visual Range Prediction Models

Teng Zhang; Xiaying Meng; Wenqiang Gao; Hongli Wang; Qinglin Niu

doi:10.3788/AOS231763

Acta Optica Sinica, Volume. 44, Issue 6, 0604001(2024)

Detectability of Low Characteristic Aircraft Based on Different Ground-Based Infrared Visual Range Prediction Models

Teng Zhang¹, Xiaying Meng², Wenqiang Gao¹, Hongli Wang³, and Qinglin Niu^1、*

Author Affiliations

¹School of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, Shanxi, China

²National Key Laboratory of Scattering and Radiation, Shanghai 201109, China

³School of Information and Communication Engineering, North University of China, Taiyuan 030051, Shanxi, China

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Figures & Tables(17)

Fig. 1. Comparison results of relationship between spatial frequency and minimum resolvable temperature difference

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Fig. 2. Computational domain and grid

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Fig. 3. Observation schematic diagram of ground-based infrared detection system

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Fig. 4. Schematic diagram of representative detection surface of aircraft^[22]

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Fig. 5. Schematic diagram of atmospheric transmittance

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Fig. 6. Contours of aircraft flow field. (a) Pressure of flow field; (b) Mach number of flow field; (c) temperature of flow field

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Fig. 7. Radiation intensities at different observation angles within detection surface 1

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Fig. 8. Radiation intensities at different observation angles within detection surface 2

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Fig. 9. Maximum detection range in long wave band of detection surface 1

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Fig. 10. Maximum detection range in medium wave band of detection surface 1

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Fig. 11. Maximum detection range in long wave band of detection surface 2

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Fig. 12. Maximum detection range in medium wave band of detection surface 2

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Fig. 13. Maximum observation range of ground-based infrared system

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Fig. 14. Maximum observation range of MRTD visual range prediction model at different observation levels

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Table 1. Comparison of calculation results of MDTD visual range prediction model
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Table 1. Comparison of calculation results of MDTD visual range prediction model
Bullet surface temperature /K 500 600 700
Distance（Ref.）/km 4.83 5.82 6.29
Distance（Cal.）/km 4.89 5.24 5.45
Error /% 1.24 9.97 13.35

Table 2. Performance parameter of Catherine-GP^[23]

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Table 2. Performance parameter of Catherine-GP^[23]

Parameter	Parameter value
Clear aperture（mm）	125.00
NETD（mK）	80
Average transmittance	0.80
Frame rate（f/sec）	25.00
Horizontal field of view wide/narrow（rad）	0.15708/0.0524
Vertical field of view wide/narrow（rad）	0.11781/0.0393
Detection element length（μm）	28
Detection element width（μm）	25
Detecting Element Numbers	228×4
Peak value D^*（cmHz^1/2W^-1）	2.3×10¹¹

Table 3. Number of half cycles (n_e) corresponding to different observation levels
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Table 3. Number of half cycles (n_e) corresponding to different observation levels
Observation level Discovery Classify Recognition Identify
n_e 2.0 2.8 8.0 12.8

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Teng Zhang, Xiaying Meng, Wenqiang Gao, Hongli Wang, Qinglin Niu. Detectability of Low Characteristic Aircraft Based on Different Ground-Based Infrared Visual Range Prediction Models[J]. Acta Optica Sinica, 2024, 44(6): 0604001

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Paper Information

Category: Detectors

Received: Nov. 8, 2023

Accepted: Jan. 2, 2024

Published Online: Mar. 15, 2024

The Author Email: Niu Qinglin (niuql@nuc.edu.cn)

DOI:10.3788/AOS231763

CSTR:32393.14.AOS231763

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Comparison of calculation results of MDTD visual range prediction model

Table 1. Comparison of calculation results of MDTD visual range prediction model

Table 2. Performance parameter of Catherine-GP[23]

Table 2. Performance parameter of Catherine-GP[23]

Table 3. Number of half cycles (ne) corresponding to different observation levels

Table 3. Number of half cycles (ne) corresponding to different observation levels

Table 2. Performance parameter of Catherine-GP^[23]

Table 2. Performance parameter of Catherine-GP^[23]

Table 3. Number of half cycles (n_e) corresponding to different observation levels

Table 3. Number of half cycles (n_e) corresponding to different observation levels