A Fast Cross-Correlation Combined with Interpolation Algorithms for the LiDAR Working in the High Background Noise
Round 1
Reviewer 1 Report
The authors present an interesting piece of work with a clear aim and outcome. The figures quality needs to be improved, because some image resolutions are too poor. In addition, Fig. 4a has no scientific meaning and is too small. It is sufficient to show one sample single with and without ensemble average in the some diagram. After revision of the figures, the authors need to be careful about the terms precision and accuracy. There is no definition of precision and accuracy as quantiative figures of merit. You can only say that A is more precise than B, but you cannot say that the precision amounts to .. . So please revise the manuscript accordingly, and give your definition of the used figure of merit (standard deviation, rms values, mean error, ....). I do recommend to look at the international guide to the expression of uncertainty in measurements known as GUM, which freely available and an international standard.
As a result, my recommendation is minor revisions.
Author Response
Dear Reviewer!
Please see the attachment
Best regard!
Thanh-Tuan Nguyen
Author Response File: 
Author Response.pdf
Reviewer 2 Report
This is a very interesting paper and the authors did a great job starting from the literature review. The methodology is clearly given and the findings are discussed in a scientific manner. I have very few comments to improve the paper.
- Some acronyms in the paper do not come with a definition when they first appear. Authors can re-visit the paper and make the corrections
- There are very few sentences to be checked to have smooth reading. I would advise a final proofreading
- Figure 11 and Figure 12: I understood giving the overall accuracy/precision for the experiments. however, it may confuse the readers since we have over 10 different distance setting that is significant in accuracy/precision. It would be better to add the worst values (least accurate/precise) reading with the recorded distances.
Author Response
Dear Reviewer!
Please see the attachment!
Best regard!
Thanh-Tuan Nguyen
Author Response File: Author Response.pdf
Reviewer 3 Report
In this paper, a fast cross-correlation(fCC) algorithm developed from the cross-correlation(CC) method in the LiDAR system working under high background noise ambient. The proposed FCC algorithm was compared with other four algorithms, such as CCP, FCCG, FCCC, and FCCS.
The paper is well organized in terms of the explanation on the proposed algorithm, and the result shows the improvement of noise cancellation clearly. However, there is somewhat lack of the explanation of commercial technology presently using. Therefore, I ask that authors need to add the performance of commercial Lidar sensor in the figure from Fig.10 to Fig.12.
Author Response
Dear Reviewer!
Please see the attachment!
Best regard!
Thanh-Tuan Nguyen
Author Response File: Author Response.pdf
