Velocity Estimation‑Based Active Aberration Modulation Moving Target Detection Method

Changchun Jiang; Shengjie Liu; Mulin Yao; Junwei Li; Hao Xian

doi:10.3788/AOS250898

Acta Optica Sinica, Volume. 45, Issue 16, 1610002(2025)

Velocity Estimation‑Based Active Aberration Modulation Moving Target Detection Method

Changchun Jiang^1,2,3, Shengjie Liu^4,5,6, Mulin Yao^1,2,3, Junwei Li^7、*, and Hao Xian^1,2,3

Author Affiliations

¹National Laboratory on Adaptive Optics, Chengdu 610209, Sichuan , China

²Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, Sichuan , China

³University of Chinese Academy of Sciences, Beijing 100049, China

⁴College of Physics and Electronic Engineering, Hainan Normal University, Haikou 571158, Hainan , China

⁵Key Laboratory of Laser Technology and Optoelectronic Functional Materials of Hainan Province, Hainan Normal University, Haikou 571158, Hainan , China

⁶Hainan International Joint Research Center for Semiconductor Lasers, Hainan Normal University, Haikou 571158, Hainan , China

⁷School of Aeronautics and Astronautics, Xihua University, Chengdu 610039, Sichuan , China

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Abstract Get PDF(in Chinese)

Figures & Tables(26)

Fig. 1. Schematic diagram of image frame sequence shift superposition

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Fig. 2. Motion vector parameter space of target

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Fig. 3. Flowchart of speed iteration estimation method

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Fig. 4. Illustration of VAMCM workflow

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Fig. 5. Image frames in dataset. (a)(c) Aberration-free frames;

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Fig. 6. Direct correlation results of low SNR image frames.

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Fig. 7. Results of the first speed iteration estimation. (a) Sampling space; (b) extreme correlation values under each estimation; (c) correlation results of optimal speed estimation

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Fig. 8. Results of the second speed iteration estimation. (a) Sampling space; (b) extreme correlation values under each estimation; (c) correlation results of optimal speed estimation

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Fig. 9. Comparison between real trajectory and detected trajectory of target. (a) X coordinate of trajectory; (b) Y coordinate of trajectory

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Fig. 10. Optimal detection probability and detection sensitivity

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Fig. 11. Optimal false alarm rate and mean trajectory absolute error

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Fig. 12. Time consumption ratio of each part of method

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Fig. 13. Comparison of detection probability. (a) Detection probability; (b) enlarged partial view of Fig. 13(a)

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Fig. 14. Comparison of detection sensitivity. (a) Detection sensitivity; (b) enlarged partial view of Fig. 14(a)

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Fig. 15. Comparison of false alarm rate. (a) False alarm rate; (b) enlarged partial view of Fig. 15(a)

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Fig. 16. Comparison of mean absolute difference of trajectory

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Fig. 17. Comparison of time consuming

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Fig. 18. Indoor moving target experiment platform. (a) Light path diagram; (b) practicality diagram

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Fig. 19. Examples of data for indoor moving target image frame sequence. (a) The 1st frame; (b) the 2nd frame; (c) the 199th frame; (d) the 200th frame

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Fig. 20. Out-field moving target experiment platform. (a) Light path diagram; (b) practicality diagram

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Fig. 21. Examples of data for out-field moving target image frame sequence. (a) The 1st frame; (b) the 2nd frame; (c) the 99th frame; (d) the 100th frame

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Fig. 22. VAMCM detection results from out-field data

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Table 1. Runtime environment configuration of computer
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Table 1. Runtime environment configuration of computer
Configuration item Configuration parameter
Central processing unit Intel Core i9-13900H
Memory DDR5 32GB
Hard-drive Samsung PM9A1 1TB
Graphics processing unit NVIDIA RTX 4060

Table 2. Performance of VAMCM under different estimation quantities
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Table 2. Performance of VAMCM under different estimation quantities
Index 3 4 5 6 7
$P_{d}$ /% 100 100 100 100 100
$P_{s}$ /% 93.25 94.45 96.30 96.30 96.30
$P_{f}$ /% 11.38 9.99 6.89 6.89 6.89
$D_{M A D T}$ /（pixel/frame） 0.5991 0.4590 0.4211 0.3970 0.3968
$t$ /s 2.0271 3.3766 5.0613 7.7447 9.6896

Table 3. Main component models of indoor moving target experimental platform

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Table 3. Main component models of indoor moving target experimental platform

Component	Model
Laser	Thorlabs CPS635R
Adaptive optics kit （L1‒L4， mirror）	Thorlabs AOK8/M
Spectroscope	Thorlabs BP108
Integrating sphere	LBTEK LBIS-LPS100-3
Halogen lamp	LBTEK LBHL2000‒20W
Deformable mirror	Thorlabs DMH40/M-P01
Imaging lens	Thorlabs MVL35M1
sCOMS detector	Thorlabs CS2100M-USB

Table 4. Performance comparison of various methods in indoor moving target experiments

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Table 4. Performance comparison of various methods in indoor moving target experiments

Method		TT	TR	ASTTV-NTLA	4DST-BTMD	VAMCM
SNR is 2.4422	P_d /%	0	0	0	0	45.0
	P_s /%	0	0	0	0	59.4
	P_f /%	100	100	100	100	40.4
	D_MADT /（pixel/frame）	‒	‒	‒	‒	1.7062
SNR is 3.7260	P_d /%	0	0	0	0	73.6
	P_s /%	0	0	0	0	61.9
	P_f /%	100	100	100	100	36.8
	D_MADT /（pixel/frame）	‒	‒	‒	‒	1.1504
SNR is 4.2294	P_d /%	0	0	0	0	85.2
	P_s /%	0	0	0	0	89.6
	P_f /%	100	100	100	100	6.58
	D_MADT /（pixel/frame）	‒	‒	‒	‒	1.0824
SNR is 4.8170	P_d /%	0	0	0	0	99.9
	P_s /%	0	0	0	0	96.2
	P_f /%	100	100	100	100	4.98
	D_MADT /（pixel/frame）	‒	‒	‒	‒	1.0320
SNR is 6.7034	P_d /%	98.7	97.8	0.90	100	100
	P_s /%	49.7	60.6	0.45	50	100
	P_f /%	50.2	50.0	99.1	50	0
	D_MADT /（pixel/frame）	1.1535	1.1671	1.027	1.1404	1.0031

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Changchun Jiang, Shengjie Liu, Mulin Yao, Junwei Li, Hao Xian. Velocity Estimation‑Based Active Aberration Modulation Moving Target Detection Method[J]. Acta Optica Sinica, 2025, 45(16): 1610002

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