Salp Swarm Algorithm-Based Kalman Filter for Seamless Multi-Source Fusion Positioning with Global Positioning System/Inertial Navigation System/Smartphones

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Summary: The paper proposes an enhanced method for seamless positioning by integrating a Global Positioning System (GPS) and an Inertial Navigation System (INS) using the Salp Swarm Algorithm (SSA). The main objective is to improve localization accuracy, particularly in indoor-outdoor (I/O) transition areas where GPS signals are weak or unavailable. The authors designed a novel Kalman filtering approach augmented with SSA for optimal switching between GPS and INS, aiming to achieve continuous and precise navigation in complex environments.

1. Introduction:

The introduction effectively establishes the significance of high-precision location services and the challenges faced in achieving seamless positioning across indoor and outdoor environments. The authors present the limitations of current methods and highlight the need for an improved solution. The introduction is clear, concise, and well-motivated.

Strengths:

• The problem is clearly defined, and the motivation for the study is well-explained.
• The review of existing techniques and their limitations provides a strong foundation for the proposed approach.

Weaknesses:

• The introduction could benefit from a more detailed discussion of related work, specifically on recent advancements in SSA or similar optimization algorithms.

2. Methodology:

The methodology is a significant strength of this paper. The authors detail the development of the SSA-based seamless positioning method, including the Kalman-filtered GPS/INS fusion model and the improved indoor-outdoor detection algorithm. The fitness function design and the SSA's role in optimizing switching parameters are well-explained, supported by mathematical formulations.

Strengths:

• The integration of SSA with Kalman filtering is innovative, addressing the issue of signal degradation in I/O transition areas effectively.
• The mathematical modeling is thorough, with clear derivations and explanations of the algorithms used.

Weaknesses:

• The methodology section is dense with technical details, which might be challenging for readers unfamiliar with the algorithms. A more accessible summary or visual aids could help in understanding the key concepts.

3. Results:

The results section presents a comprehensive evaluation of the proposed method through experiments conducted in real-world scenarios. The performance of the SSA-based method is compared with existing techniques, demonstrating superior accuracy in I/O detection and positioning.

Strengths:

• The experimental setup is well-designed, with clear descriptions of the conditions under which the tests were conducted.
• The results are presented with appropriate figures and tables, showing a clear improvement in accuracy over existing methods.

Weaknesses:

• While the results are promising, the paper could include more diverse test scenarios to further validate the robustness of the proposed method. For example, testing in different environmental conditions (e.g., urban vs. rural) could provide additional insights.

4. Discussion:

The discussion appropriately interprets the results, emphasizing the advantages of the SSA-based approach in handling the challenges of seamless I/O positioning. The authors also acknowledge the limitations and suggest areas for future work.

Strengths:

• The discussion effectively ties the experimental results back to the research questions, highlighting the practical implications of the findings.
• The authors are transparent about the limitations of their study, which is commendable.

Weaknesses:

• The discussion could delve deeper into the potential impact of SSA parameter settings on the algorithm's performance and how these might be optimized for different use cases.

5. Contribution to the Field:

This paper makes a valuable contribution to the field of seamless positioning and navigation. The integration of SSA with GPS/INS systems offers a novel solution to a well-known problem, potentially influencing future research in the area of high-precision location services.

Strengths:

• The proposed method addresses a critical challenge in navigation and positioning, offering a solution that is both innovative and practical.
• The combination of optimization algorithms with navigation systems is a forward-thinking approach that could inspire further research and development.

Weaknesses:

• The paper could better position its contribution within the broader context of research on optimization algorithms for navigation, drawing connections to similar approaches or alternative techniques.

Overall Evaluation:

The paper is well-structured, with a clear and logical flow from problem definition to solution and evaluation. The proposed SSA-based seamless positioning method represents a significant advancement in the field, particularly in improving accuracy in challenging I/O environments. While the paper is technically sound, it could benefit from a more accessible presentation of the methodology and a broader discussion of its implications.

Recommendation:

• Accept with Minor Revisions: I recommend the paper be accepted with minor revisions, particularly in enhancing the accessibility of the methodology section and expanding the discussion of the algorithm's broader impact and potential applications.

Author Response

Dear Reviewer,

We would like to thank the you for giving us an opportunity to revise our manuscript (remotesensing-3131960), and we also appreciate you very much for your the valuable comments and suggestions. We have carefully revised the manuscript according to these comments and suggestions. In general, we have tried our best to revise our manuscript and provide the point-by-point responses. Attached please find our responses to the referees’ comments.

The main revisions are summarized briefly as follows:

1) We have streamlined the abstracts to provide a concise summary of background descriptions and experimental results to ensure that key points and research contributions are included while maintaining content integrity and brevity.

2) We have revised and re-organized the contribution and motivation of this paper in the introduction section to clearly present the value and significance of our research;

3) To increase the logic of the KL divergence improvement Kalman filtering part, we refined the derivation of the original Eqs. (34) to (39).

4) We have reviewed the images in the article and corrected the problem of missing units or missing captions in some of the images.

5) We have given more comparison with existing methods.

6) We have analyzed the simulation results more comprehensively;

7) To enhance our background description, we have incorporated several references.

These additional citations are denoted as [34], [35],[36] and [39] in the manuscript.

The details can be found in the following responses, and the revisions are highlighted in blue in “remotesensing-3131960-modified.pdf”.

In response to the reviewers' comments on the quality of the language, we have thoroughly refined the manuscript using MDPI's English editing to improve the accuracy and fluency of the language presentation, proof of which can also be found in the Appendix. The revised version now presents our findings more clearly. Thank you for your attention to this detail, and we are confident that these changes will enhance the overall quality of the paper.

Thank you and best regards.

 

Sincerely yours,

The Authors.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

See file.

Comments for author File: Comments.pdf

Comments on the Quality of English Language

The manuscript should avoid ambiguous expressions in writing. For instance, in the abstract, the phrase "which is superior to conventional methods" is too vague, as it does not specify in which performance metrics the proposed method outperforms traditional approaches.

Author Response

Dear Reviewer,

We would like to thank the you for giving us an opportunity to revise our manuscript (remotesensing-3131960), and we also appreciate you very much for your the valuable comments and suggestions. We have carefully revised the manuscript according to these comments and suggestions. In general, we have tried our best to revise our manuscript and provide the point-by-point responses. Attached please find our responses to the referees’ comments.

The main revisions are summarized briefly as follows:

1) We have streamlined the abstracts to provide a concise summary of background descriptions and experimental results to ensure that key points and research contributions are included while maintaining content integrity and brevity.

2) We have revised and re-organized the contribution and motivation of this paper in the introduction section to clearly present the value and significance of our research;

3) To increase the logic of the KL divergence improvement Kalman filtering part, we refined the derivation of the original Eqs. (34) to (39).

4) We have reviewed the images in the article and corrected the problem of missing units or missing captions in some of the images.

5) We have given more comparison with existing methods.

6) We have analyzed the simulation results more comprehensively;

7) To enhance our background description, we have incorporated several references.

These additional citations are denoted as [34], [35],[36] and [39] in the manuscript.

The details can be found in the following responses, and the revisions are highlighted in blue in “remotesensing-3131960-modified.pdf”.

In response to the reviewers' comments on the quality of the language, we have thoroughly refined the manuscript using MDPI's English editing to improve the accuracy and fluency of the language presentation, proof of which can also be found in the Appendix. The revised version now presents our findings more clearly. Thank you for your attention to this detail, and we are confident that these changes will enhance the overall quality of the paper.

Thank you and best regards.

 

Sincerely yours,

The Authors.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

In this manuscript, the authors propose an alternative approach that integrates an improved Kalman-filtered seamless global positioning system with an inertial navigation system based on the Salp Swarm Algorithm for both indoor and outdoor positioning. The authors conclude that this approach provides more reliable and continuous location services than conventional methods such as the immutable threshold method, machine learning-based region recognition methods, and the traditional GPS/INS system. Although the manuscript has significant language issues, the workload is substantial, and the analysis and results of the manuscript seem to be correct. Therefore, I recommend major revisions before this manuscript is accepted for publication. See my comments below:

 

1. Title Revision: The current title may need to be revised for greater accuracy. There appears to be a conflict as the title refers to GPS/INS positioning, yet the abstract refers to GPS/INS as one of the conventional methods and data utilized is not solely from GPS/INS systems.

 

2. Clarification in Figure 1: What is the meaning of “food sources” depicted in Figure 1?

 

3. Lines 238-239 contain pseudo-code for the SSA, which appears redundant. Consider removing it or relocating it to an appendix.

 

4. Equations 18-19: the "0" can be omitted from the matrix representation.

 

5. Figure Clarity: Figures 5, 6, 8, 9, 11, and 14 are difficult to understand due to the lack of units, clear coordinate names, or figure titles. Additionally, an unusual font is used in Figure 7, and Chinese characters appear in Figure 14. Please review all figures for accuracy of data and text, ensure a uniform font format, and add coordinate origins if missing.

 

6. Ensure that all formulas comply with standard conventions. Vectors and matrices should be in bold italics, while scalars should be in regular font. Non-compliance may hinder readability.

 

7. Line 375: Please specify the cut-off frequency for the low-pass filter. Additionally, lines 375-376 should include a more detailed explanation of the data cleaning method used.

 

8. In table 1, the frequency of GNSS is listed as 1 Hz, but this typically refers to GNSS signal frequencies, such as L1 carrier phase frequency of 1575.42 MHz. Do you mean the sampling rate? If GPS was used for outdoor positioning, did you only measure satellite numbers and signal-to-noise ratio? Did you use dual-frequency carrier phase or code, and was PPP or RTK employed? Please revise for clarity and specify the GPS positioning method used.

 

9. The manuscript use “GNSS” sensors, which includes GPS, BeiDou, GLONASS, and Galileo. Are you utilizing data from multiple GNSS constellations or just GPS? Please clarify and revise accordingly.

 

10. Figure 3: the best score in Figure 3 appears to be zero after 10 iterations. Modify the figure for more accurate representation or consider presenting the data in a table instead.

11. Table 3 should include the exact method names referenced rather than just the corresponding reference numbers. What is the “complexities” meaning? “complexities” is different for all methods which contrary to the law of control variables.

 

12. Figure 11: It would be beneficial to add a compass to indicate the northbound direction, aiding in the interpretation of the data.

 

13. Line 424: There are some perturbations in the ("X", "Y") coordinates around (100,1205). Please revise.

14: Line 428-433: Given that the manuscript uses “GPS-assisted INS combined with improved Kalman filtering,” there may be a misinterpretation suggesting that the low INS bias is due to the addition of GPS. It is widely accepted that GPS signals are typically too weak indoors to significantly contribute to positioning, especially on smartphones with small GPS antennas. Please revise or analyze the GPS measurement quality if you maintain that the low INS bias is due to GPS.

 

15: Line 434: The phrase "correct the positioning error and make the error converge stably" is unclear. Are you referring to signal convergence?

 

16: The specific figures of 3.18 m, 4.95 m, and 6.86 m are overly precise given the larger positioning errors. Please use appropriate significant figures.

 

17. Include the names of the positioning methods compared in the conclusion.

 

18. Why was only GPS used for outdoor positioning, rather than combining GPS with INS? Many researches have demonstrated that integrating INS can improve the convergence and accuracy of GPS positioning.

Comments on the Quality of English Language

Extensive editing of English language required.

Author Response

Dear Reviewer,

We would like to thank the you for giving us an opportunity to revise our manuscript (remotesensing-3131960), and we also appreciate you very much for your the valuable comments and suggestions. We have carefully revised the manuscript according to these comments and suggestions. In general, we have tried our best to revise our manuscript and provide the point-by-point responses. Attached please find our responses to the referees’ comments.

The main revisions are summarized briefly as follows:

1) We have streamlined the abstracts to provide a concise summary of background descriptions and experimental results to ensure that key points and research contributions are included while maintaining content integrity and brevity.

2) We have revised and re-organized the contribution and motivation of this paper in the introduction section to clearly present the value and significance of our research;

3) To increase the logic of the KL divergence improvement Kalman filtering part, we refined the derivation of the original Eqs. (34) to (39).

4) We have reviewed the images in the article and corrected the problem of missing units or missing captions in some of the images.

5) We have given more comparison with existing methods.

6) We have analyzed the simulation results more comprehensively;

7) To enhance our background description, we have incorporated several references.

These additional citations are denoted as [34], [35],[36] and [39] in the manuscript.

The details can be found in the following responses, and the revisions are highlighted in blue in “remotesensing-3131960-modified.pdf”.

In response to the reviewers' comments on the quality of the language, we have thoroughly refined the manuscript using MDPI's English editing to improve the accuracy and fluency of the language presentation, proof of which can also be found in the Appendix. The revised version now presents our findings more clearly. Thank you for your attention to this detail, and we are confident that these changes will enhance the overall quality of the paper.

Thank you and best regards.

 

Sincerely yours,

The Authors.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

All my comments have been accepted. I recommend accepting in current version.