Intelligent QLFEKF Integrated Navigation for the SSBE Cruise Phase Based on X-Ray Pulsar/Solar and Target Planetary Doppler Information Fusion

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Novel work with rigorous deduction.

Author Response

Dear Reviewers,

We would like to thank you for your careful reading, helpful comments, and constructive suggestions, which have significantly improved the presentation of our manuscript. We have made a thorough revision of our manuscript and responded to all comments detailly and carefully, and the detailed corrections are listed below. Your comments are laid out below in highlighted in yellow font and specific concerns have been numbered. For response to comments from you, our response is given in italicized font below and changes / additions to the manuscript are given in the red text.

Thank you for your helpful comments that helped us to revise our manuscript. All co-authors have proofread the manuscript to make every effort to ensure that the revised manuscript is more readable. We hope that the minor revised manuscript complies with the journal's editorial and professional standard.

 

Yours sincerely,

 

Corresponding author: Jihe Wang

E-mail: wangjihe@mail.sysu.edu.cn

Novel work with rigorous deduction.

Response: We thank the reviewer for reading our paper carefully and giving very favorable comments. We have also done a thorough examination of our manuscript again, hoping to make it more perfect.

 

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The considerations presented in the presented article are important from the point of view of the development of space exploration. The scientific investigations carried out are at a high level of mathematics and engineering. A minor drawback of the graphics are figures 5 and 6, which are difficult to read. The range of displayed values ​​can be narrowed on vertical soybeans, which will allow the obtained result to be read more precisely. the remaining elements of the scientific article meet the requirements. 

Author Response

Dear Reviewers,

We would like to thank you for your careful reading, helpful comments, and constructive suggestions, which have significantly improved the presentation of our manuscript. We have made a thorough revision of our manuscript and responded to all comments detailly and carefully, and the detailed corrections are listed below. Your comments are laid out below in highlighted in yellow font and specific concerns have been numbered. For response to comments from you, our response is given in italicized font below and changes / additions to the manuscript are given in the red text.

Thank you for your helpful comments that helped us to revise our manuscript. All co-authors have proofread the manuscript to make every effort to ensure that the revised manuscript is more readable. We hope that the minor revised manuscript complies with the journal's editorial and professional standard.

 

Yours sincerely,

 

Corresponding author: Jihe Wang

E-mail: wangjihe@mail.sysu.edu.cn

The considerations presented in the presented article are important from the point of view of the development of space exploration. The scientific investigations carried out are at a high level of mathematics and engineering. A minor drawback of the graphics are figures 5 and 6, which are difficult to read. The range of displayed values can be narrowed on vertical soybeans, which will allow the obtained result to be read more precisely. the remaining elements of the scientific article meet the requirements.

Response: Thank you very much for your comments on some important problems that still remain in the manuscript. In order to improve the readability of figures 5 and figures 6, we modified figures 5 and redrew figures 6 and figures 7-9. For details, see the part marked in red on pages 15, 18-20 of our manuscript.

 

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

Review Report

Summary
A novel Q-learning-based extended Kalman filter (QLFEKF) is introduced that is focused on autonomous navigation specifically applicable to deep-space missions. The limitation of traditional navigation methods, which are the missing ground-based tracking in real-time are the main problems that need to be circumvented. The described methodology includes q learning in the used Kalman filter improving both the accuracy and adaptability of the navigation. The paper’s showcases this methodology given an innovative approach and reinforcement learning integration together with detailed simulation results.

General Concept Comments

The manuscript tackles a major challenge present in deep-space navigation. Historically used techniques are limited due to the large distances and lack of real-time communication. This paper argues that Q-learning can improve the capabilities a Kalman filter so to overcome this problem. Ideally this paper could be improved by providing more details on the computational resources and requirements needed to implement the proposed QLFEKF algorithm in a real-world mission scenario. The constraints that follow from deep-space missions often lead to limit the algorithm that can be used in a realistic scenario. Furthermore, even though the proposed method shows promising results in simulations, this study could benefit from more faithful mission perturbations, such as environmental noise and signal fluctuations.

Article Comments

1. Testing: The presented hypothesis can be mostly tested through the specified simulations. This shows the improvements delivered by the algorithm’s performance. However, to enhance trustworthiness, it would be good to explore the how changes in mission environments, (e.g.: increased or decreased signal noise or Doppler measurement instability) could change the performance. Adding such further analysis of these aspects would strengthen assurance in the robustness to real-world scenarios.



2. Methodology: The details are sound and clearly explain how the QLFEKF is designed. More emphasis should be put on the description of the tuning of Q-learning parameters, especially the learning rate and discount factor. These parameters are fundamental to the algorithm’s performance and stability. It would be helpful for the authors to clarify how these values were selected and deliberate their impact on navigation accuracy.
3. Reproducibility: While the paper outlines the QLFEKF setup clearly and provides the essential equations, more exhaustive explanations of initial parameter settings, especially the noise covariance matrices, are necessary to enhance reproducibility. This level of detail would enable other researchers to replicate the study or apply the method in similar navigation contexts.
4. Computational Feasibility: Deep-space missions often face limitations in processing power. Therefore, it would be valuable to assess the computational load of the QLFEKF algorithm and determine if it is feasible for real-time application on space probes. Discussing the trade-offs between computational efficiency and estimation accuracy would provide clarity on the method’s practicality.
5. Simulation Setup: The simulation results provide compelling evidence for the QLFEKF’s effectiveness. However, to gain a more comprehensive understanding of the algorithm’s robustness, the authors should explicitly state the environmental assumptions used in the simulations. Specifically, it would be beneficial to specify the conditions, such as signal noise and environmental disturbances, and simulate additional scenarios with varying degrees of measurement noise or environmental uncertainties. This approach would offer a more nuanced view of the algorithm’s performance under different conditions.


Review Comments on Literature

1. Completeness and Relevance: The literature review covers the major advancements in autonomous navigation, Kalman filtering, and reinforcement learning applications for navigation. However, it would be advantageous to include references to recent Journal of Remote Sensing articles or other works related to adaptive filtering and machine learning applications in similar remote sensing and autonomous navigation contexts. This would enhance the manuscript’s relevance and firmly position it within the journal’s audience.
2. Knowledge Gap: The authors effectively identify the gap in autonomous navigation methods for missions beyond ground communication range and make a compelling case for the QLFEKF’s potential. However, the review could be enhanced by comparing the QLFEKF with recent adaptive or machine learning-based filtering approaches, further emphasizing its uniqueness and highlighting its advantages over competing methods.
3. Appropriateness of References: Most references are current and relevant to the topic, with no excessive self-citations. Including additional citations from similar reinforcement learning or adaptive filtering applications in space environments would strengthen the background and provide more context for the Q-learning approach.

Specific Comments

• Line 45-50: Provide a reference for the challenges facing deep-space missions, focus on long-distance navigation and communication delays.
• Line 120-130: Expand the description of the simulation environment. This would clarify assumptions about measurement noise and environmental disturbances to help explain the conditions under which the algorithm was tested.
• Figure 3 (Information Interaction Model): Add labels to Figure 3 -> clarify the Q-learning and Kalman filter integration processes to improve reader comprehension.
• Parameter Notation (Equations 1-5): Clearly define variables upon first use.
• Results Section (Simulation Data): Add a table summarizing the QLFEKF’s key performance metrics in comparison to traditional FEKF. This helps to compare the methods to other relevant methods and would clarify the proposed approach's benefits.

General Questions and Evaluation

1. Clarity and Structure: The manuscript is well-structured and generally clear, though some sections, particularly the methods, would benefit from added detail on parameter choices.
2. References: The cited references are recent and relevant, with no excessive self-citation. Including a few more sources from the Journal of Remote Sensing would increase contextual relevance.
3. Scientific Soundness: The manuscript is scientifically sound with a solid experimental design, though computational feasibility and simulation realism should be addressed to support practical application.
4. Reproducibility: The results are reproducible, though adding more detail on parameter settings and assumptions would aid replication.
5. Figures and Data Presentation: The figures are informative but could benefit from clearer labeling. The data interpretation is consistent, but additional performance metrics for the QLFEKF would be helpful.

Summary of Recommendations

Autonomous navigation in deep-space environments can be improved by addressing methodological transparency, computational feasibility, and situating the work within recent literature.

Comments on the Quality of English Language

The English language quality is generally good, with a clear presentation of complex technical ideas. However, some sections would benefit from minor grammatical edits and improvements in phrasing to enhance readability. In particular, simplifying sentence structures in the methodology and results sections could improve flow and reader comprehension. Addressing these minor language adjustments will make the paper more accessible to a broad scientific audience.

Author Response

Dear Reviewers,

We would like to thank you for your careful reading, helpful comments, and constructive suggestions, which have significantly improved the presentation of our manuscript. We have made a thorough revision of our manuscript and responded to all comments detailly and carefully, and the detailed corrections are listed below. Your comments are laid out below in highlighted in yellow font and specific concerns have been numbered. For response to comments from you, our response is given in italicized font below and changes / additions to the manuscript are given in the red text.

Thank you for your helpful comments that helped us to revise our manuscript. All co-authors have proofread the manuscript to make every effort to ensure that the revised manuscript is more readable. We hope that the minor revised manuscript complies with the journal's editorial and professional standard.

 

Yours sincerely,

 

Corresponding author: Jihe Wang

E-mail: wangjihe@mail.sysu.edu.cn

 

Summary

A novel Q-learning-based extended Kalman filter (QLFEKF) is introduced that is focused on autonomous navigation specifically applicable to deep-space missions. The limitation of traditional navigation methods, which are the missing ground-based tracking in real-time are the main problems that need to be circumvented. The described methodology includes q learning in the used Kalman filter improving both the accuracy and adaptability of the navigation. The paper’s showcases this methodology given an innovative approach and reinforcement learning integration together with detailed simulation results.

Response: Thank you very much for taking the time to patiently put forward valuable comments on publication of our manuscript. These opinions are of great help to the improvement of the quality of our papers.

General Concept Comments

The manuscript tackles a major challenge present in deep-space navigation. Historically used techniques are limited due to the large distances and lack of real-time communication. This paper argues that Q-learning can improve the capabilities a Kalman filter so to overcome this problem. Ideally this paper could be improved by providing more details on the computational resources and requirements needed to implement the proposed QLFEKF algorithm in a real-world mission scenario. The constraints that follow from deep-space missions often lead to limit the algorithm that can be used in a realistic scenario. Furthermore, even though the proposed method shows promising results in simulations, this study could benefit from more faithful mission perturbations, such as environmental noise and signal fluctuations.

Response: Thank you very much for asking this question, which is also the research hotspot and focus of our great concern. Since the solar system boundary exploration is divided into several flight phases, this paper mainly conducts simulation analysis for the scene of the long-term cruise phase. The setting of environmental noise and signal fluctuations of this mission is designed according to the specific scene of the long-term cruise phase, and the environmental noise in other subsequent flight phases (such as the gravity assist flight phase) will be very different from that in this paper. We're already doing research on that.

Article Comments

1. Testing: The presented hypothesis can be mostly tested through the specified simulations. This shows the improvements delivered by the algorithm’s performance. However, to enhance trustworthiness, it would be good to explore the how changes in mission environments, (e.g.: increased or decreased signal noise or Doppler measurement instability) could change the performance. Adding such further analysis of these aspects would strengthen assurance in the robustness to real-world scenarios.

Response: Thank you very much for your question about the test simulation, which is of great help to our research. In this paper, we mainly focus on the accuracy of intelligent navigation methods, without detailed consideration of the navigation effect and robustness under the condition of increasing or reducing signal noise or Doppler measurement instability that you mentioned. In the future research, we have specifically studied it as a key issue of the research. We hope that the later work can achieve your expected results on the test. If you are interested, please feel free to contact us for more in-depth communication.

2. Methodology: The details are sound and clearly explain how the QLFEKF is designed. More emphasis should be put on the description of the tuning of Q-learning parameters, especially the learning rate and discount factor. These parameters are fundamental to the algorithm’s performance and stability. It would be helpful for the authors to clarify how these values were selected and deliberate their impact on navigation accuracy.

Response: Thank you very much for your worthwhile comments and suggestions. The Q-learning parameters in the manuscript have been analyzed and verified in detail, and different learning rates and discount factors have been set. According to the principle of Q-learning algorithm, these parameters are selected within their reasonable value range. For example, the learning rate is usually set at 0.1. The range of learning rate we selected was around 0.1 to ensure the validity of the value and to better verify the degree of its influence on navigation accuracy. It is explained in detail in lines 532-542 and 617-621 of the manuscript.

3. Reproducibility: While the paper outlines the QLFEKF setup clearly and provides the essential equations, more exhaustive explanations of initial parameter settings, especially the noise covariance matrices, are necessary to enhance reproducibility. This level of detail would enable other researchers to replicate the study or apply the method in similar navigation contexts.

Response: Thank you for your valuable comments on the QLFEKF algorithm. The initial parameters are set according to the uncertain perturbations such as the central celestial body perturbation and solar light pressure of the cruise phase dynamic state and the characteristics of the measurement sensor and the observed data. The state noise covariance matrix includes the quantization of the uncertainty of the system dynamic model, mainly considering the influence of the cruise phase perturbation. The measurement noise covariance matrix is a random error in the measurement process, which can be obtained by analyzing the pulsar TOA observation accuracy. In the autonomous intelligent navigation of the solar system boundary exploration, the uncertainty of system dynamic model, actual observation data, sensor characteristics and environmental factors should be taken into account in the setting of state and measurement noise covariance matrix. These matrices not only need to be precisely set at the initial stage of the navigation system design, but also need to be dynamically adjusted to adapt to environmental changes during the operation of the navigation system. We hope that our answers will clear up your doubts and make you feel satisfied.

4. Computational Feasibility: Deep-space missions often face limitations in processing power. Therefore, it would be valuable to assess the computational load of the QLFEKF algorithm and determine if it is feasible for real-time application on space probes. Discussing the trade-offs between computational efficiency and estimation accuracy would provide clarity on the method’s practicality.

Response: Thank you very much for your attention and comments on the real-time computation of QLFEKF algorithm. This question is what we need to focus on in the course of our research. It is important to evaluate the computational load and the feasibility of real-time application of QLFEKF algorithm in the navigation missions at the solar system boundary exploration. The QLFEKF algorithm combines Q-learning and traditional federated filtering, and can optimize state parameters in real time. This combination means that QLFEKF can dynamically adjust state and observed noise covariance parameters through Q-learning mechanism to optimize filtering performance while maintaining high estimation accuracy. Compared with traditional FEKF integrated navigation algorithm, QLFEKF improves the navigation accuracy of position and velocity state estimation by 55.84% and 37.04%, respectively. This indicates that QLFEKF may increase the computational load while improving the accuracy, but the increase in the load is worthwhile, and the navigation system can also carry it. At the same time, it provides higher accuracy and stronger autonomous intelligent integrated navigation capabilities to meet the needs of the solar system boundary exploration navigation.

5. Simulation Setup: The simulation results provide compelling evidence for the QLFEKF’s effectiveness. However, to gain a more comprehensive understanding of the algorithm’s robustness, the authors should explicitly state the environmental assumptions used in the simulations. Specifically, it would be beneficial to specify the conditions, such as signal noise and environmental disturbances, and simulate additional scenarios with varying degrees of measurement noise or environmental uncertainties. This approach would offer a more nuanced view of the algorithm’s performance under different conditions.

Response: Thank you very much for your thoughtful comments. This paper focuses on the research of the cruise phase of the solar system boundary exploration, whose environmental disturbance and measurement noise are set according to the operating environment and measurement sensor of the cruise phase, so the scene we study is only the cruise phase, and the simulated noise and environment are also targeted at the cruise phase. Based on this, the performance of the state estimation algorithm is studied. We are currently working on other scenarios, and our research will enable intelligent integrated high-precision navigation of multi-stage, multi-scenario at the solar system boundary exploration.

Review Comments on Literature

1. Completeness and Relevance: The literature review covers the major advancements in autonomous navigation, Kalman filtering, and reinforcement learning applications for navigation. However, it would be advantageous to include references to recent Journal of Remote Sensing articles or other works related to adaptive filtering and machine learning applications in similar remote sensing and autonomous navigation contexts. This would enhance the manuscript’s relevance and firmly position it within the journal’s audience.

Response: Thank you very much for your suggestion, and I'm sorry that we did not consider it carefully through. We should more quote the recent articles in the Journal of Remote Sensing or other works related to the application of adaptive filtering and machine learning in similar remote sensing and autonomous navigation environments, which have been quoted in this paper through in-depth reading and learning of relevant works in this journal. For details, see references [31], [32] and [37] in the revised manuscript. The corresponding modification content is as follows:

Revised version:

(1) Line 124-127, 774-779

EKF is widely used in various types and scenarios of navigation system, and has a very stable navigation performance[31, 32]. This state estimation algorithm has also been favored by researchers from the field of astronomical autonomous navigation.

[31] Xin, S. J.; Wang, X. M.; Zhang, J. L.; Zhou, K.; Chen, Y. F. A Comparative Study of Factor Graph Optimization-Based and Extended Kalman Filter-Based PPP-B2b/INS Integrated Navigation[J]. Remote Sensing, 2023, 15(21): 5144. https://doi.org /10.3390/rs15215144.

[32] Yin, Z. H.; Yang, J. C.; Ma, Y.; Wang, S. L.; Chai, D. S.; Cui, H. N. A Robust Adaptive Extended Kalman Filter Based on an Improved Measurement Noise Covariance Matrix for the Monitoring and Isolation of Abnormal Disturbances in GNSS/INS Vehicle Navigation[J]. Remote Sensing, 2023, 15(17): 4125. https://doi.org/10.3390/rs15174125.

(2) Line 147-148, 788-789

The intelligent autonomous navigation algorithm combined with the reinforce-ment Q-learning has been favored by researchers at present[37].

[37] Ou, J. J; Guo, X.; Lou, W. J.; Zhu, M. Quadrotor autonomous navigation in semi-known environments based on deep rein-forcement learning[J]. Remote Sensing, 2021, 13(21): 4330. https://doi.org/10.3390/rs13214330.

2. Knowledge Gap: The authors effectively identify the gap in autonomous navigation methods for missions beyond ground communication range and make a compelling case for the QLFEKF’s potential. However, the review could be enhanced by comparing the QLFEKF with recent adaptive or machine learning-based filtering approaches, further emphasizing its uniqueness and highlighting its advantages over competing methods.

Response: Thank you very much for your comments. This is a question we explored in depth in our last paper, See the paper " Intelligent navigation for the cruise phase of solar system boundary exploration based on Q-learning EKF" in the journal of Complex & Intelligent Systems from the Website https://link.springer.com/article/10.10 07/s40747-023-01286-y.

3. Appropriateness of References: Most references are current and relevant to the topic, with no excessive self-citations. Including additional citations from similar reinforcement learning or adaptive filtering applications in space environments would strengthen the background and provide more context for the Q-learning approach.

Response: Thank you very much for your meticulous comments. We have added relevant literature on the application of similar reinforcement learning or adaptive filtering techniques in space environments in this paper, specifically see the revised manuscript, and the revised content is as follows.

Revised version:

Line 148-150, 791-792

In particular, it has a very good application in estimating the accuracy of spacecraft in-orbit, which can provide high precision for spacecraft navigation system[38].

[38] Xiong, K.; Zhao, Q.; Yuan, L. Calibration Method for Relativistic Navigation System Using Parallel Q-Learning Extended Kalman Filter[J]. Sensors, 2024, 24(19): 6186. https://doi.org/10.3390/s24196186.

Specific Comments

1. Line 45-50: Provide a reference for the challenges facing deep-space missions, focus on long-distance navigation and communication delays.

Response: Thank you very much for your suggestion, we also think this issue is important. Lines 45-50 mainly introduce the background of the solar system boundary exploration and the development situation of solar system boundary exploration, and we have detailed explanations on long-distance navigation and communication delays in lines 54-62. In order to provide reference for the challenges faced by deep space missions, the content you mentioned has been supplemented and revised in detail, as follows.

Revised version:

Line 54-62

Due to the characteristics of the unknown and varied exploration environment, super-long space exploratory distance, super-long orbital flight time, and super-large communication data delay between the SSBE and the ground, the implementation of the exploration mission is very difficult[10]. Because the probe is so far from the ground, it cannot rely on traditional navigation ways, such as the radio station aeronutical ground, very-long-baseline interferometry (VLBI) and tracking telemetry and command (TT&C) communication network to provide real-time and high accuracy navigation information, which presents new challenges to ultra-long distance autonomous high accuracy navigation[11, 12].

2. Line 120-130: Expand the description of the simulation environment. This would clarify assumptions about measurement noise and environmental disturbances to help explain the conditions under which the algorithm was tested.

Response: Thank you so much for your unique insights and comments. We really did not consider this aspect of the problem, and we have clarified the measurement noise and environmental disturbances in the paper, as detailed below.

Revised version:

Line 127-132

In addition, for the navigation system, the measurement noise is usually based on the measurement error of the sensor used in the actual task. The environmental disturbances mainly come from micrometeoroids, cosmic rays and solar wind in the space environment, which will have an impact on the navigation accuracy of the deep space probe, and it is necessary to set the noise according to the scene in the different phase during the simulation analysis.

3. Figure 3 (Information Interaction Model): Add labels to Figure 3 -> clarify the Q-learning and Kalman filter integration processes to improve reader comprehension.

Response: Thank you very much for asking this question. In Figure 3, we mainly show how the probe agent interacts with the environment to realize the information interaction between the probe and the environment, and the information is used as the input parameters of the filter part. Subsequently, we have given the specific flow and implementation of the filter data and parameters in the navigation process of the probe agent. Therefore, the integration process of Q-learning and EKF is described in Algorithm 1 and Figure 5. We hope the above reply will be able to answer your questions.

4. Parameter Notation (Equations 1-5): Clearly define variables upon first use.

Response: Thank you very much for your valuable advice, but we have neglected this problem. We have examined equations 1- 5 in detail and made a complete supplement to the relevant definition of variables, as detailed in the red marks on pages 5-7 of our manuscript.

5. Results Section (Simulation Data): Add a table summarizing the QLFEKF’s key performance metrics in comparison to traditional FEKF. This helps to compare the methods to other relevant methods and would clarify the proposed approach's benefits.

Response: Thank you very much for your comments. The comparison between STD/XP-QLFEKF proposed in this paper and traditional STD/XP-EKF has been given in detail in Table 5. STD/XP represents multiple measurement models adopted, and the comparison of specific methods is shown in Table 5 for position and velocity estimation accuracy values. The detailed description and analysis of the method in this paper are shown in lines 607-623.

General Questions and Evaluation

1. Clarity and Structure: The manuscript is well-structured and generally clear, though some sections, particularly the methods, would benefit from added detail on parameter choices.

Response: Thank you very much for your comments. In terms of parameter selection, we have made corresponding replies to the above questions, and the setting details of corresponding parameters are also provided in Section 4.1 of the manuscript. We hope we have answered to your satisfaction.

2. References: The cited references are recent and relevant, with no excessive self-citation. Including a few more sources from the Journal of Remote Sensing would increase contextual relevance.

Response: Thank you very much for your special comments. We have also quoted relevant papers in Remote Sensing journal at corresponding places in the manuscript, which makes our paper more readable and complete. The cited papers can be seen in the content marked red in the references.

3. Scientific Soundness: The manuscript is scientifically sound with a solid experimental design, though computational feasibility and simulation realism should be addressed to support practical application.

Response: Thank you very much for your recognition, we will gradually improve in the subsequent work, and hope to realize simulation and application in specific engineering projects to verify the actual usability of our proposed method.

4. Reproducibility: The results are reproducible, though adding more detail on parameter settings and assumptions would aid replication.

Response: Thank you very much for your prompt and affirmation, our method has good accuracy, and the results are also repeatable.

5. Figures and Data Presentation: The figures are informative but could benefit from clearer labeling. The data interpretation is consistent, but additional performance metrics for the QLFEKF would be helpful.

Response: Thank you very much for your comments. We have modified the labels in Figure 6-9 to ensure the clarity and readability of the labels in the figure. For details, see pages 18-20 of the paper.

Author Response File: Author Response.docx

Reviewer 4 Report

Comments and Suggestions for Authors

 

1. The future exploration of the solar system boundary in China poses new challenges for navigation, which differ from Tianwen-1, China's farthest planet exploration mission to date and the only one that has been achieved. These new difficulties need to be analyzed and compared in the introduction section. Should compare the differences with the following paper“End-to-end Mars entry, descent, and landing modeling and simulations for Tianwen-1 guidance, navigation, and control system”

 

2. To use X-ray Pulsar / Solar to solve navigation problem, not only can be used for Solar System Boundary Exploration, but also Interplanetary exploration, the paper need to say the difference of technical requirement for Solar System Boundary Exploration and interplanetary exploration.

 

3. The location of the example section is approximately several times the distance between the sun and the Earth (AU), and it needs to be explained why this was chosen.

Author Response

Dear Reviewers,

We would like to thank you for your careful reading, helpful comments, and constructive suggestions, which have significantly improved the presentation of our manuscript. We have made a thorough revision of our manuscript and responded to all comments detailly and carefully, and the detailed corrections are listed below. Your comments are laid out below in highlighted in yellow font and specific concerns have been numbered. For response to comments from you, our response is given in italicized font below and changes / additions to the manuscript are given in the red text.

Thank you for your helpful comments that helped us to revise our manuscript. All co-authors have proofread the manuscript to make every effort to ensure that the revised manuscript is more readable. We hope that the minor revised manuscript complies with the journal's editorial and professional standard.

 

Yours sincerely,

 

Corresponding author: Jihe Wang

E-mail: wangjihe@mail.sysu.edu.cn

1. The future exploration of the solar system boundary in China poses new challenges for navigation, which differ from Tianwen-1, China's farthest planet exploration mission to date and the only one that has been achieved. These new difficulties need to be analyzed and compared in the introduction section. Should compare the differences with the following paper “End-to-end Mars entry, descent, and landing modeling and simulations for Tianwen-1 guidance, navigation, and control system”.

Response: Thank you for your kind suggestions and comments. There is no direct relationship between the cruise phase navigation of the solar system boundary exploration and the entry, descent and landing phases navigation of the Mars exploration. First of all, in terms of mission, they are completely different missions, the solar system boundary exploration belongs to the edge of the solar system and beyond the space exploration, Tianwen-1 is to Mars landing exploration. Secondly, their flight phases are completely different. In addition, the measurements they use to navigate are also completely different. Therefore, it is not possible to conduct comparative analysis through simulation. Generally speaking, in the process of studying the solar system boundary exploration, it is only necessary to conduct flyby exploration of planetary bodies and do not need to land on planetary bodies, so it will not involve the navigation of entry, descent and landing phases proposed by you. In the introduction, we give a detailed description of the current on-orbit mission and research related to the solar system boundary exploration. This paper only studies the navigation of the cruise phase of the solar system boundary exploration mission. We hope that our reply will satisfy you and dispel your concerns.

2. To use X-ray Pulsar / Solar to solve navigation problem, not only can be used for Solar System Boundary Exploration, but also Interplanetary exploration, the paper need to say the difference of technical requirement for Solar System Boundary Exploration and interplanetary exploration.

Response: Thank you very much for your comments. Thank you very much for your opinion, which is also a problem that we need to consider when studying the navigation technology of the solar system boundary exploration. The differences between the technical requirements of solar system boundary exploration and interplanetary exploration are mainly reflected in the exploration distance, environmental conditions, energy supply, communication technology and scientific payloads, and exploration targets. Their differences are as follows:

First, the solar system boundary exploration involves extremely long distances, such as the Voyager 1 and 2 probes that took nearly 40 years to reach the solar system boundary. Interplanetary exploration usually refers to exploration in the inner solar system, such as between planets or between planets and the sun, at relatively short distances. The solar system boundary exploration needs to consider the long-duration flight, which puts higher requirements on the life span and stability of the probe.

Second, the solar system boundary exploration faces an extremely cold, dark environment, and far from the sun, so it needs to solve the problem of power supply in extremely dark conditions. Interplanetary exploration will also encounter extreme environments, but conditions may be mild compared to solar system boundary exploration.

Third, the solar system boundary exploration due to the distance from the sun, solar energy access is reduced, so it is necessary to consider new efficient energy and propulsion technologies such as nuclear batteries. Interplanetary exploration may rely more on solar energy, or use traditional energy technologies between planets close to the sun.

Fourth, the solar system boundary exploration requires ultra-long distance measurement and control communication technology, because the communication signal between the probe and the earth will decay with the increase of distance. Although interplanetary exploration also requires long-distance communication, the communication distance and signal attenuation problems are not as serious as the solar system boundary exploration.

In addition, the solar system boundary exploration aims to explore the mechanism of solar wind propagation and evolution in the heliosphere, as well as the interplanetary dust clouds that retain clues to the birth of life and the initially solar system. Interplanetary exploration focuses more on the characteristic and environments of planets, asteroids, comets and other objects in the solar system.

In conclusion, the solar system boundary exploration needs to break through orbit design, new efficient energy and propulsion technologies, ultra-long distance measurement and control communication, new scientific payloads and other cutting-edge space technologies. Interplanetary exploration technology requirements are also high, but may be more focused on the improvement and application of existing technology.

3. The location of the example section is approximately several times the distance between the sun and the Earth (AU), and it needs to be explained why this was chosen.

Response: Many thanks to the reviewers for their meticulous questions and comments. We chose this location in the simulation mainly to ensure that the communication between the probe and the ground takes a long period of time, so as to ensure that the probe does not rely on the ground navigation measurement and control means to study the autonomous navigation of the solar system boundary exploration, and provide a reasonable navigation scheme for the autonomous navigation technology of the solar system boundary exploration and continuous flight to 100AU.

Author Response File: Author Response.docx