A novel signal denoising framework (EEMD-VMD-IMWOA) for Rayleigh lidar is proposed to better suppress noise in an atmospheric lidar echo signal and improve retrieval accuracy. The ensemble empirical mode decomposition (EEMD) is used to retain the intrinsic mode functions (IMFs) of signal as the low-frequency effective component. Based on the denoising ability of variational mode decomposition (VMD) under high noise signal, the IMFs with noise is further denoised by VMD to obtain high-frequency effective component, wherein the improved whale optimization algorithm (IMWOA) is used to get the optimal decomposition layer K and the quadratic penalty α of VMD. Then, the low-frequency and high-frequency effective components are reconstructed to gain denoised signal. The simulation results show that the denoising effect of EEMD-VMD-IMWOA is superior to Wavelet threshold, EEMD and VMD, especially the far-field noise interference can be suppressed. Under the condition that the temperature retrieval error is less than ± 10 K, when the integration time is only 600s, the effective retrieval altitude can reach 59.6km, which is 17.3% higher than that without denoising. Finally, the retrieval accuracy of the measured lidar signal is significantly improved by EEMD-VMD-IMWOA.
To enhance the correlation in the orthogonal directions, a polarization self-modulation scheme with an intra-cavity quarter wave plate in a coaxial pumping orthogonally polarized laser was proposed. This quasi-isotropic cavity was compared with the traditional scheme in terms of the differences in the oscillation between dual components and the intra-cavity eigenstate distribution was obtained. Both theoretical and experimental results indicated that modes were effectively locked in TE and TM directions and dual-eigenstates output was achieved, which provided a half-free-spectrum-range frequency difference in ±45° directions. Q-switching and dual-wavelength-operation did not affect the polarization self-modulation process.
In order to obtain a passively Q-switched sub-nanosecond microchip laser with a low pulse jitter of less than 10 ns, a scheme of injection-seeding stable nanosecond laser pulses was designed. The pulse timing jitter of the passively Q-switched laser was improved from μs-level to ns-level with the seeding pulse energy of around 70 μJ. Based on experimental measurements, the dynamic process of pulse locking by varying the seeding pulse energy was discussed. The locking threshold affected by the peak pump power and time delay (ΔtQ) between the initial passively Q-switched laser and seeding pulses was also analyzed.
KEYWORDS: Air temperature, LIDAR, Atmospheric sensing, Monte Carlo methods, Measurement uncertainty, Temperature metrology, Statistical analysis, Atmospheric modeling
The measurement uncertainty is an important parameter to evaluate the reliability of the Rayleigh lidar in detecting atmospheric temperature. This presentation aims to study the atmospheric temperature measurement uncertainty of a floating platform-mounted Rayleigh lidar. A model was established for altitude correction considering the platform attitude, and the temperature uncertainty originating from the fluctuation in rolling and pitching angles was evaluated using the Monte Carlo method (MCM). The results show that the atmospheric temperature uncertainty due to platform fluctuation is confined to 10-2 K when the detection altitude is up to 65 km.
The atmospheric temperature measurement uncertainty was evaluated by the Guide to the Expression of Uncertainty in Measurement (GUM) method. The lidar measurement model was introduced considering the design of actual lidar instruments and standard retrieval method. The detection noise and the auxiliary temperature uncertainty were considered as two main uncertainty sources. Based on the simulation data of Rayleigh scattering lidar operating at 532 nm with 2- hour integration period, it was calculated that two main uncertainty sources resulted temperature standard uncertainties of around 2 K and 5 K at 60 km, respectively, and the combined standard uncertainty was 6 K.
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