An Improved Adaptive Sparrow Search Algorithm for TDOA-Based Localization

Round 1

Reviewer 1 Report

This paper proposes an improved adaptive sparrow search algorithm to solve the localization problem through noisy TDOA measurements. The results indicate that the IASSA algorithm achieves higher localization accuracy than previous methods. Also, the IASSA algorithm requires fewer iterations, which overcomes the problem of the long computation time of the swarm intelligence optimization algorithm. The topic is interesting and widely recognized in localization research methods. There are still some minor problems in the paper that need to be modified.

1.authors should clarify what improvement they are proposing in this paper, in the main section.

2.By changing the area of the experimental area, are the authors still able to obtain the same algorithmic results?

3.The conclusion part should be more refined to make the findings and contributions of the paper clearer. Furthermore, please note the difference between the conclusions and the abstract.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

The improved adaptive sparrow search algorithm (IASSA) based on the sparrow search algorithm (SSA) is proposed in this manuscript. Generally, the manuscript is well written and contains all the necessary elements. The topic is current and interesting for the reader. The theoretical model is adequately presented. However, a few sentences should be added in the head of the simulation results, which will define the practical implementations methodology of the experiments more closely (for example, Figure 2 is unclear, number of Monte Carlo runs (if you use them), random generator parameters, etc).

Adaptive methods generally do not provide significant improvements to the algorithms, so it is necessary to state exactly what the contribution of the proposed algorithm is, in comparison to 'competitive' algorithms?

Suggestions for improvement the manuscript:

• Define practical implementations, more closely.
• State exactly what the contribution of the proposed algorithm is, in comparison to 'competitive' algorithms.

The improved adaptive sparrow search algorithm (IASSA) based on the sparrow search algorithm (SSA) is proposed in this manuscript. Generally, the manuscript is well written and contains all the necessary elements. The topic is current and interesting for the reader. The theoretical model is adequately presented. However, a few sentences should be added in the head of the simulation results, which will define the practical implementations methodology of the experiments more closely (for example, Figure 2 is unclear, number of Monte Carlo runs (if you use them), random generator parameters, etc).

Adaptive methods generally do not provide significant improvements to the algorithms, so it is necessary to state exactly what the contribution of the proposed algorithm is, in comparison to 'competitive' algorithms?

After the minor changes made, the manuscript can be referred into the further procedure for publication.

Suggestions for improvement the manuscript:

• Define practical implementations, more closely.
• State exactly what the contribution of the proposed algorithm is, in comparison to 'competitive' algorithms.

Review (for Author):

The improved adaptive sparrow search algorithm (IASSA) based on the sparrow search algorithm (SSA) is proposed in this manuscript. Generally, the manuscript is well written and contains all the necessary elements. The topic is current and interesting for the reader. The theoretical model is adequately presented. However, a few sentences should be added in the head of the simulation results, which will define the practical implementations methodology of the experiments more closely (for example, Figure 2 is unclear, number of Monte Carlo runs (if you use them), random generator parameters, etc).

Adaptive methods generally do not provide significant improvements to the algorithms, so it is necessary to state exactly what the contribution of the proposed algorithm is, in comparison to 'competitive' algorithms?

Suggestions for improvement the manuscript:

• Define practical implementations, more closely.
• State exactly what the contribution of the proposed algorithm is, in comparison to 'competitive' algorithms.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 3 Report

1. The description of the algorithm is too textbook like. It will be good if the whole algorithm can be explained based on a practical case, e.g 3 receivers, 1 tag.

2. Can this algorithm be used for motion scenario as well or only stationary cases? Because in the experiment, the tag is fixed. If yes, please provide also the motion results, some tracked trajectories would be nice. If not please think about how it can be extended to motion tracking. In practice the stationary localization is rarely used.

3. How do you calculate the position/coordinate of the receivers? As far as I know the biggest issue for infrastructure based localization/tracking system is that the location of receivers must be measured beforehand. It would be great if the locations of receivers can be obtained automatically.

Author Response

Please see the attachment.

Author Response File: Author Response.docx