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Asymmetric spatial heterodyne spectroscopy is a new ultra-high resolution remote sensing detection technology. Based on its features of large luminous flux, small size, and high precision, it is very suitable for high-precision detection in a deep-space environment. Because of its high sensitivity, various details may interfere with the measurement results in experiment. In this paper, from the perspective of experimental condition, considering the influences of factors such as fringe center position offset, uneven illumination, and Gaussian noise, a compound optical path difference phase-shift solution method is proposed. The simulation calculation and data analysis show that the offset of the nominal point relative to the center position will significantly affect the systematic error of spectral velocity measurement. And the compound optical path difference phase-shift solution method can smooth the environmental noise and random interference to a certain extent. For the interference fringe image with 1% Gaussian noise, the velocity measurement error can be controlled within 5‰ by using the compound optical path difference phase-shift solution method, which makes the asymmetric spatial heterodyne spectroscopy technology more suitable for space optoelectronic precision measurement.
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Keywords:
- Doppler asymmetric spatial heterodyne spectral velocimetry /
- deep space exploration /
- compound optical path difference phase-shift solution method /
- nominal point deviation
[1] 薛喜平, 张洪波, 孔德庆 2017 天文研究与技术 14 382
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Xue X P, Zhang H B, Kong D Q 2017 ART 14 382
Google Scholar
[2] 张伟 2018 飞控与探测 1 41
Google Scholar
Zhang W 2018 Flight Control Detect. 1 41
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Jiang T, Shi H L, Shen J, Dai H S, Xiong W 2018 Acta Photon. Sin. 47 1
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Kuang Y L, Fang L, Peng X, Cheng X, Zhang H, Liu E H 2018 Acta Phys. Sin. 67 140703
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Google Scholar
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Peng X, Zhang W 2017 Acta Photon. Sin. 46 1
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Wang X Q, Xiong W, Ye S, Zhang L J 2013 J. Appl. Opt. 34 79
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图 1 DASH光谱仪结构示意图
Figure 1. Schematic diagram of DASH spectrometer.
图 2 相位差随光程差变化示意图
Figure 2. Schematic diagram of phase changing with optical path difference.
图 3 探测器视场对应不同的干涉条纹数据段示意图
Figure 3. Schematic diagram of different interference fringe data segments.
图 4 实测干涉条纹图像
Figure 4. Measured interference fringe image.
图 5 发射线信号的多普勒频移示意图
Figure 5. Schematic diagram of Doppler shift of emission line signal.
图 6 多普勒频移对应的空间域波形变化示意图
Figure 6. Schematic diagram of waveform change in spatial domain.
图 7 基于DFT算法的传统解算方法和复合光程差相移解算方法所得多普勒速度的对比图
Figure 7. Comparison of Doppler velocity obtained from traditional solution method and COPS method by DFT algorithm.
图 8 经调制后的模拟干涉条纹图像
Figure 8. Modulated analog interference fringe image.
图 9 多行像素波形序列的多普勒速度示意图 (a) 传统DFT算法; (b) 自适应频率跟踪算法
Figure 9. Schematic diagram of Doppler velocity of multi line pixel waveform sequence: (a) Traditional DFT algorithm; (b) adaptive frequency tracking (AFT) algorithm.
图 10 两种方法计算100组高斯噪声条纹图像所得多普勒速度差值的对比
Figure 10. Comparison of Doppler velocity obtained from 100 groups of Gaussian noise fringe images calculated by the two methods.
表 1 仿真计算所得速度值及误差
Table 1. Speed value and error calculated by simulation
v0/(m·s–1) v1/(m·s–1) v2/(m·s–1) $ \Delta {v_1} $/(m·s–1) $ \Delta {v_2} $/(m·s–1) 10000.0 9991.3 10000.8 8.7 0.8 
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表 2 不同算法及处理方法对干涉图解算所得速度值的对比
Table 2. Comparison of velocity values calculated on interferograms by different algorithms and processing methods.
v0/(m·s–1) DFT AFT v1/(m·s–1) v2/(m·s–1) v1/(m·s–1) v2/(m·s–1) Mean 10000.0 13989.4 9370.3 10003.8 10001.9 Std 0 516.0 212.6 225.8 185.9
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[1] 薛喜平, 张洪波, 孔德庆 2017 天文研究与技术 14 382
Google Scholar
Xue X P, Zhang H B, Kong D Q 2017 ART 14 382
Google Scholar
[2] 张伟 2018 飞控与探测 1 41
Google Scholar
Zhang W 2018 Flight Control Detect. 1 41
Google Scholar
[3] Englert C R, Harlander J M, Babcock D D, Stevens M H, Siskind D E 2006 Atmospheric Optical Modeling, Measurement and Simulation II San Diego, US, August 15–16, 2006 p63030T
[4] Englert C R, Babcock D D, Harlander J M 2007 Appl. Opt. 46 7297
Google Scholar
[5] Harlander J M, Englert C R, Babcock D D, Roesler F L 2010 Opt. Express 18 26430
Google Scholar
[6] Englert C R, Harlander J M, Emmert J T, Babcock D D, Roesler F L 2010 Opt. Express 18 27416
Google Scholar
[7] Solheim B, Brown S, Sioris C, Shepherd G 2015 Atmos. Ocean 53 50
Google Scholar
[8] 姜通, 施海亮, 沈静, 代海山, 熊伟 2018 光子学报 47 1
Google Scholar
Jiang T, Shi H L, Shen J, Dai H S, Xiong W 2018 Acta Photon. Sin. 47 1
Google Scholar
[9] 李志伟, 熊伟, 施海亮, 罗海燕, 乔延利 2016 光谱学与光谱分析 36 2291
Google Scholar
Li Z W, Xiong W, Shi H L, Luo H Y, Qiao Y L 2016 Spectrosc. Spect. Anal. 36 2291
Google Scholar
[10] 况银丽, 方亮, 彭翔, 程欣, 张辉, 刘恩海 2018 物理学报 67 140703
Google Scholar
Kuang Y L, Fang L, Peng X, Cheng X, Zhang H, Liu E H 2018 Acta Phys. Sin. 67 140703
Google Scholar
[11] Harlander J M, Englert C R, Marr K D, Harding B, Chu K 2019 Appl. Opt. 58 3606
Google Scholar
[12] Zhang Y F, Feng Y T, Fu D, Wang P C, Sun J, Bai Q L 2020 Chin. Phys. B 29 298
Google Scholar
[13] 彭翔, 张嵬 2017 光子学报 46 1
Google Scholar
Peng X, Zhang W 2017 Acta Photon. Sin. 46 1
Google Scholar
[14] 王新强, 熊伟, 叶松, 张丽娟 2013 应用光学 34 79
Google Scholar
Wang X Q, Xiong W, Ye S, Zhang L J 2013 J. Appl. Opt. 34 79
Google Scholar
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