Review of 'Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays'

Reviewer: Ahmad Bazzi

Publication date of review: 2024-12-20

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Ahmad Bazzi5

Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated ArraysScienceOpen

Very interesting read

Average rating:	    Rated 5 of 5.
Level of importance:	    Rated 5 of 5.
Level of validity:	    Rated 5 of 5.
Level of completeness:	    Rated 4 of 5.
Level of comprehensibility:	    Rated 5 of 5.
Competing interests:	None

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Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays

Baptiste Chatelier, José Miguel Mateos-Ramos, Vincent Corlay … (2024)

Direction of arrival (DoA) estimation is a common sensing problem in radar, sonar, audio, and wireless communication systems. It has gained renewed importance with the advent of the integrated sensing and communication paradigm. To fully exploit the potential of such sensing systems, it is crucial to take into account potential hardware impairments that can negatively impact the obtained performance. This study introduces a joint DoA estimation and hardware impairment learning scheme following a model-based approach. Specifically, a differentiable version of the multiple signal classification (MUSIC) algorithm is derived, allowing efficient learning of the considered impairments. The proposed approach supports both supervised and unsupervised learning strategies, showcasing its practical potential. Simulation results indicate that the proposed method successfully learns significant inaccuracies in both antenna locations and complex gains. Additionally, the proposed method outperforms the classical MUSIC algorithm in the DoA estimation task.

0 comments Cited 0 times     Rated -3 of 5. – based on 1 reviews

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ScienceOpen disciplines: Numerical methods,Information systems & theory,Artificial intelligence,Electrical engineering

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The paper introduces a differentiable version of the MUSIC algorithm, termed diffMUSIC, for anglee of arrival estimation while learning and compensating for hardware impairments such as antenna location inaccuracies and complex gain variations.
The paper is interesting and I have some minor comments:

Perhaps it would be good to include in the introduction sources of imperfections like mutual coupling for example [R1,R2].
Why was Jain’s metric chosen over others like entropy or variance-based measures?
Why does SubspaceNet fail in the case of multiple sources ? If so, can it be adapted for the multi source case ?
Fig. 4 is beautiful - is it averaged over many Monte Carlo trials or is this one spectrum per method ?

References

[R1] A. Bazzi, D. T. M. Slock and L. Meilhac, “Online angle of arrival estimation in the presence of mutual coupling,” 2016 IEEE Statistical Signal Processing Workshop (SSP), Palma de Mallorca, Spain, 2016, pp. 1-4, doi: 10.1109/SSP.2016.7551749

[R2] A. Bazzi, D. T. M. Slock and L. Meilhac, “On mutual coupling for ULAs: Estimating AoAs in the presence of more coupling parameters,” 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA, 2017, pp. 3361-3365, doi: 10.1109/ICASSP.2017.7952779

Review of 'Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays'

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Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays

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