Application of multiple signal classification algorithm to frequency estimation in coherent dual-frequency lidar

Ruixiao Li; Kun Li; Changming Zhao

doi:10.1117/12.2295527

12 January 2018 Application of multiple signal classification algorithm to frequency estimation in coherent dual-frequency lidar

Ruixiao Li, Kun Li, Changming Zhao

Author Affiliations +

Proceedings Volume 10619, 2017 International Conference on Optical Instruments and Technology: Advanced Laser Technology and Applications; 1061909 (2018) https://doi.org/10.1117/12.2295527
Event: International Conference on Optical Instruments and Technology 2017, 2017, Beijing, China

Abstract

Coherent dual-frequency Lidar (CDFL) is a new development of Lidar which dramatically enhances the ability to decrease the influence of atmospheric interference by using dual-frequency laser to measure the range and velocity with high precision. Based on the nature of CDFL signals, we propose to apply the multiple signal classification (MUSIC) algorithm in place of the fast Fourier transform (FFT) to estimate the phase differences in dual-frequency Lidar. In the presence of Gaussian white noise, the simulation results show that the signal peaks are more evident when using MUSIC algorithm instead of FFT in condition of low signal-noise-ratio (SNR), which helps to improve the precision of detection on range and velocity, especially for the long distance measurement systems.

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Ruixiao Li, Kun Li, and Changming Zhao "Application of multiple signal classification algorithm to frequency estimation in coherent dual-frequency lidar", Proc. SPIE 10619, 2017 International Conference on Optical Instruments and Technology: Advanced Laser Technology and Applications, 1061909 (12 January 2018); https://doi.org/10.1117/12.2295527

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