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.
© 2005 Optical Society of America
Original Manuscript: June 11, 2004
Revised Manuscript: September 29, 2004
Manuscript Accepted: October 13, 2004
Published: February 20, 2005
Hai-Tao Fang, De-Shuang Huang, and Yong-Hua Wu, "Antinoise approximation of the lidar signal with wavelet neural networks," Appl. Opt. 44, 1077-1083 (2005)