A Real-Time UWB-Based Device-Free Localization and Tracking System

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper proposes a real-time Device-Free Localization and Tracking (DFLT) system leveraging Ultra-Wideband (UWB) sensors to overcome the limitations of narrowband sensing networks, especially in indoor environments. Field experiments were conducted in both indoor and outdoor environments to evaluate and compare the UWB-based DFLT system against traditional multi-channel narrowband DFLT (CD-NRTI) solutions.

Very best results in multipath immunity and localization and tracking accuracy. In conclusion, the paper demonstrates that the proposed UWB-based DFLT system significantly outperforms traditional narrowband DFLT solutions in terms of accuracy and robustness, particularly in challenging indoor environments with rich multipath interference. The use of LoS path RSS measurements in UWB is shown to be particularly beneficial in heavily obstructed settings.

Excellent work. congratulations.

Author Response

We thank the reviewer for the positive comment. We have made every effort to improve the quality in the revised manuscript.

Reviewer 2 Report

Comments and Suggestions for Authors

This paper proposes a real-time DFLT system leveraging UWB sensors to estimate target-induced shadowing using two UWB RSS measurements with significantly accuracy and robustness. However, there are some suggestions for improvement:

1. The current literature review in Section 2 predominantly relies on references that are 5-10 years old, or in some cases, even older. The field of UWB technology has evolved dramatically in recent years. The authors should incorporate recent research from the last 3-5 years.
2. In section 3, line 187, How do these two assumptions be implemented in the UWB signal measurement process?
3. What is the bandwidth of the experiment in Figure 1? The experimental range is only 6 meters, is the experimental conclusion applicable to a larger experimental environment? What is the specific difference between Figure 1.d and Figure 1.e, please indicate it.
4. What does m mean in equation 14?
5. In section 5, What are the selection conditions for bandwidth and the number of base stations? How do you collect the ground truth of the movement trajectory?

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This manuscript proposes a real-time UWB-based device-free localization and tracking system, demonstrating clear advantages over narrowband systems, particularly in multipath-rich environments. The methodology is solid, and the experiments are comprehensive and convincing. Here are some minor suggestions.

1. Please provide more details on the leading-edge detection method and its robustness. A brief comparison with CIR-based methods would be helpful.
2. Add a short paragraph discussing the challenges and potential solutions for extending the system to multi-target scenarios.

Author Response

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Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

Device-free localization and tracking behave important significance in location based service. Experiments were conducted for show the performance. However, several issues should be solved before considering publication.

1. At the first of the introduction section, WiFi, Zigbee, RFID, Bluetooth are introduced. Strong suggestion is introducing several references, such as 'IDWPSOInLoc', 'ZigBee-based intelligent indoor positioning','Sing-Ant', 'SPOTTER'.
2. Two categories of DFLT techniques are not appropriate. From the subsequent description and my personal understanding, fingerprint-based techniques belong to model-based. Fingerprint-based technique using machine learning or deep learning always map RSS values into models.
3. The first letter of the title should be capitalized.
4. A total of 16 nodes were deployed in a 40-square-meter area. The accuracy of the positioning results is not very high. Try to discuss whether the conclusions obtained can support a large-scale area.

Author Response

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Author Response File: Author Response.pdf

Reviewer 5 Report

Comments and Suggestions for Authors

1. The introduction briefly mentions the proposed UWB-based system. Can you provide a more detailed explanation of its system architecture, such as sensor placement strategy, real-time data flow, and processing pipeline?

2.  How does your method specifically improve upon previous UWB-based DFLT systems, such as those using CIR or particle filters? Are there quantitative or computational comparisons available?

3. In the indoor experiments, it seems that 16 UWB nodes were deployed in a relatively small area (e.g., 6.6 × 6 m). Could you comment on whether this density reflects a practical deployment scenario, or if it was intended for evaluation purposes only?

4. Have you evaluated or simulated the system's performance in larger environments, such as open halls? How would the system scale in terms of tracking accuracy and latency?

5. How accurate and stable are the RSS measurements in your UWB modules under typical indoor interference, such as WiFi or moving people?

6. Have you evaluated the impact of key hyperparameters in RTI on the final localization accuracy?

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

All previous review comments have been addressed, and there are no further suggestions at this time.

Reviewer 5 Report

Comments and Suggestions for Authors

Thank you for your effort. My comments have been fully addressed.