Antinoise approximation of the lidar signal with wavelet neural networks.
We propose a new, to our knowledge, denoising method for lidar signals based on a regression model and a wavelet neural network (WNN) that permits the regression model not only to have a good wavelet approximation property but also to make a neural network that has a self-learning and adaptive capability for increasing the quality of lidar signals. Specifically, we investigate the performance of the WNN for antinoise approximation of lidar signals by simultaneously addressing simulated and real lidar signals. To clarify the antinoise approximation capability of the WNN for lidar signals, we calculate the atmosphere temperature profile with the real signal processed by the WNN. To show the contrast, we also demonstrate the results of the Monte Carlo moving average method and the finite impulse response filter. Finally, the experimental results show that our proposed approach is significantly superior to the traditional methods.
Fang
Hai-Tao
HT
Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China. fanght@iim.ac.cn
Huang
De-Shuang
DS
Wu
Yong-Hua
YH