Dai Huixing, Gao Chunqing, Lin Zhifeng, Wang Kaixin, Zhang Xu
Appl Opt. 2021 Dec 1;60(34):10721-10726. doi: 10.1364/AO.442716.
A denoising method based on singular value decomposition (SVD) and variational mode decomposition (VMD) is proposed for wind lidar. Utilizing the covariance matrix based lidar signal simulation model, the performance of VMD, SVD, and VMD-SVD is evaluated. The results show that the VMD-SVD method is of better performance, and the output signal-to-noise ratio (SNR) is about 12 dB at the input SNR of -9. The actual lidar signals processing is performed with this combined denoising method, and the detection range and wind speed at pulse accumulation numbers of 50,100, and 300 are compared. We set the wind speed resulting from noisy signal with pulse accumulation number of 300 as the reference wind speed, and the mean value and standard deviation of wind differences are analyzed. The results show that the denoising method can not only increase the detection range while ensuring the accuracy of wind speed estimation but also achieve the same detection distance with fewer pulse accumulations, thereby improving the temporal resolution. For the pulse accumulation number of 50, the detection range is extended to 24 km from 18.45 km, and the standard deviation of speed difference is 0.88 m/s; for the same detection range, the temporal resolution is increased by about 6 times.
提出了一种基于奇异值分解（SVD）和变分模态分解（VMD）的用于风激光雷达的去噪方法。利用基于协方差矩阵的激光雷达信号仿真模型，对VMD、SVD和VMD - SVD的性能进行了评估。结果表明，VMD - SVD方法具有更好的性能，在输入信噪比为 -9时，输出信噪比约为12 dB。使用这种组合去噪方法对实际激光雷达信号进行处理，并比较了脉冲累积数为50、100和300时的探测范围和风速。我们将脉冲累积数为300的噪声信号所得到的风速设为参考风速，并分析了风速差异的平均值和标准差。结果表明，该去噪方法不仅能在保证风速估计精度的同时增加探测范围，还能以更少的脉冲累积数达到相同的探测距离，从而提高时间分辨率。对于脉冲累积数为50的情况，探测范围从18.45 km扩展到24 km，速度差异的标准差为0.88 m/s；在相同探测范围内，时间分辨率提高了约6倍。
