Paper Info
Title
A Novel De-Noising Method for Improving the Performance of Full-Waveform LiDAR Using Differential Optical Path.
Abstract
A novel de-noising method for improving the performance of full-waveform light detection and ranging (LiDAR) based on differential optical path is proposed, and the mathematical models of this method are developed and verified. Backscattered full-waveform signal (BFWS) is detected by two avalanche photodiodes placed before and after the focus of the focusing lens. On the basis of the proposed method, some simulations are carried out and conclusions are achieved. (1) Background noise can be suppressed effectively and peak points of the BFWS are transformed into negative-going zero-crossing points as stop timing moments. (2) The relative increment percentage of the signal-to-noise ratio based on the proposed method first dramatically increases with the increase of the distance, and then the improvement gets smaller by increasing the distance. (3) The differential Gaussian fitting with the Levenberg-Marquardt algorithm is applied, and the results show that it can decompose the BFWS with high accuracy. (4) The differential distance should not be larger than c/2 x (rmin), and two variable gain amplifiers can eliminate the inconsistency of two differential beams. The results are beneficial for designing a better performance full-waveform LiDAR.
Year
DOI
Venue
2017
10.3390/rs9111109
REMOTE SENSING
Keywords
Field
DocType
full-waveform LiDAR,differential optical path,background noise,SNR,backscattered full-waveform signal,Levenberg-Marquardt
Optical path,Avalanche photodiode,Background noise,Waveform,Remote sensing,Optics,Gaussian,Lidar,Ranging,Mathematical model,Mathematics
Journal
Volume
Issue
ISSN
9
11
2072-4292
Citations 
PageRank 
References 
1
0.43
8
Authors
8
Name
Order
Citations
PageRank
Yang Cheng120.78
Jie Cao245.28
Qun Hao360.99
Yuqing Xiao410.43
Fanghua Zhang5125.97
Wenze Xia610.43
Kaiyu Zhang731.15
Haoyong Yu862174.47