Innovation Adaptive UKF Train Location Method Based on Kinematic Constraints

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper presents a train positioning method that employs an adaptive UKF with kinematic constraints to enhance accuracy in GPS-denied environments. By integrating INS and Odometer data, the method significantly improves positioning estimates. Simulation results validate its effectiveness in enhancing navigation accuracy. While the paper is well-written and introduces a practical approach, I have the following comments:

- Please provide more specifics about the simulation environment and parameters used, such as the types of scenarios tested and the conditions under which the simulations were conducted.

- A description of the sensor characteristics (e.g., accuracy, noise levels, and sampling rates) is necessary, as well as how the algorithm adapts to these characteristics during operation.

- Since the use of dynamic kinematic constraints is a central aspect of the paper, it would be beneficial to clarify how these constraints improve error compensation in the INS/ODO model.

- It would be helpful to include any insights into why vertical positioning is less accurate compared to horizontal positioning, addressing potential factors that contribute to this discrepancy.

- A more robust justification for choosing the UKF over alternatives like EKF or PF should be provided, focusing on the specific advantages of the UKF in this application context, possibly through numerical results.

Author Response

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Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The Authors address important topic of enhancing the positioning accuracy of railway vehicles. The methods proposed in the paper uses additional kinematic constraints to improve the position estimation accuracy using the inertial navigation sensors/data and odometry fusion in the absence (or poor quality) of satellite reference signals (e.g. in tunnels). The evaluation results have been compared to state of the art reference methods using computer simulations executed on measurement data. The results show advantages of the proposed approach compared to the reference method.

 

The paper can be improved in the following apects:

- state of the art analysis - numerous sensor fusion techniques have been widely used in land mobile navigation. In car navigation, the task is even more complex (the roads and possible movement paths are not so strictly defined as in case of a railroad system), and various sensor fusion techniques have been widely applied to support the vehicle navigation. The introduction should also refer to this wider range of soultions for land vehicle navigation (are the accuracies of the solutions comparable to those taken as reference?).

- How (or if?) can the proposed method be generalized to be applied for example with cars, or autonomous aerial vehicles?

- What is the computational complexity of the proposed approach compared to the reference method? What are the requirements for the computing unit to use this approach in real-time applications?

- Verification and analysis - it is recommended to specify the measurement setup in more detail. What devices were used to collect the experimental data? What is the influence of technical parameters of those sensors on the performance (accuracy) of the algorithm?

- Have you considered also using constraints related to geometry of the rail tracks (the train cannot freely move outside the track) to improve the positioning results?

 

 

Comments on the Quality of English Language

Only minor mistakes can be foud. Thorugh spell cheking and proofreading is recommended to improve the quality of the language.

Author Response

Please see the attachment.

Author Response File: Author Response.docx