A Topology Optimization Method for Reducing Communication Overhead in the Kalman Consensus Filter
Round 1
Reviewer 1 Report
- It is an original, up-to-date and relevant work. I recommend its publication, with some minor improvements, for the better readability of the text.
- The summary is interesting and concise. However, there is no methodology. It hardly goes from the introduction of the problem to the results. It would be good to follow the scheme introduction-methodology-results-conclusions.
- I think the bibliographical references are solid and varied, but I would recommend increasing the state of the question, with 6-8 references, more current (from 2021), and on the subject in question or tangential issues to the research.
- The conclusions are too brief, lacking in depth, and do not go into the limitations encountered and the prospects for research, open to the scientific community.
- Neither at the beginning nor at the end is it explained why this work is original and represents a breakthrough for the scientific community. It is clear that it is, but it should be clearly stated, especially in a journal as prestigious as this one.
- For the study to be replicable and to obtain more citations, it would be advisable to make one or two figures from the conclusions.
Author Response
Please see the attachment.
Author Response File: 
Author Response.docx
Reviewer 2 Report
1) As the paper is very timely and covers Kalman Consensus Filtering (KCF) and that have a lot of applications in a number of research topics such as Autonomous Vehicles, Indoor Positioning System (IPS), Wearable, Visual Tracking, Underwater, and Trace applications, etc. Couples of lines should be added in the Introduction section to highlight the importance of the topics as the proposed technique will be suitable for other research areas. I have included a couple of papers to cite
Few papers on AV, IPS, Wearable, and Visual Tracking suggesting Kalman Filtering
1) Ahangar, M.N.; Ahmed, Q.Z.; Khan, F.A.; Hafeez, M. A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges. Sensors 2021, 21, 706. https://doi.org/10.3390/s21030706
2)Pascacio, P.; Casteleyn, S.; Torres-Sospedra, J.; Lohan, E.S.; Nurmi, J. Collaborative Indoor Positioning Systems: A Systematic Review. Sensors 2021, 21, 1002. https://doi.org/10.3390/s21031002
3) Liu, L.; Wang, H.-H.; Qiu, S.; Zhang, Y.-C.; Hao, Z.-D. Paddle Stroke Analysis for Kayakers Using Wearable Technologies. Sensors 2021, 21, 914. https://doi.org/10.3390/s21030914
4) Chen, H.; Zhang, W.; Yan, D. Robust Visual Tracking with Reliable Object Information and Kalman Filter. Sensors 2021, 21, 889. https://doi.org/10.3390/s21030889
5) Cario, G.; Casavola, A.; Gagliardi, G.; Lupia, M.; Severino, U. Accurate Localization in Acoustic Underwater Localization Systems. Sensors 2021, 21, 762. https://doi.org/10.3390/s21030762
6) Shamsfakhr, F.; Motroni, A.; Palopoli, L.; Buffi, A.; Nepa, P.; Fontanelli, D. Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach †. Sensors 2021, 21, 717. https://doi.org/10.3390/s21030717
7) Abbas, W.B.; Che, F.; Ahmed, Q.Z.; Khan, F.A.; Alade, T. Device Free Detection in Impulse Radio Ultrawide Bandwidth Systems. Sensors 2021, 21, 3255. https://doi.org/10.3390/s21093255.
Please cite these papers as an indication of Kalman filtering for different applications.
2) In equations it should be eta^T(k) not eta(k)^T, Please change all the equations in the paper where transpose is involved.
3) Why the initial conditions (5,15,10) Are used. Please clarify these assumptions.
4) Figure 6 and 7 shows that a large number of iterations are required. This may be a problem in real case scenario as the environment changes quickly.
Author Response
Please see the attachment.
Author Response File: Author Response.docx
Round 2
Reviewer 2 Report
The authors have made the required corrections and addressed all my points.
