Convolutional Neural Networks for Direction of Arrival Estimation Compared to Classical Estimators and Bounds
Recently, there has been a proliferation of applied machine learning (ML) research, including the use of convolutional neural networks (CNNs) for direction of arrival (DoA) estimation. With the increasing amount of research in this area, it is important to balance the performance and computational c...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10872920/ |
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| author | Christopher J. Bell Kaushallya Adhikari Andrew Brown |
| author_facet | Christopher J. Bell Kaushallya Adhikari Andrew Brown |
| author_sort | Christopher J. Bell |
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| description | Recently, there has been a proliferation of applied machine learning (ML) research, including the use of convolutional neural networks (CNNs) for direction of arrival (DoA) estimation. With the increasing amount of research in this area, it is important to balance the performance and computational costs of CNNs with classical methods of DoA estimation such as Multiple Signal Classification (MUSIC). We outline the performance of both methods of DoA estimation for single-source and two-source cases for multiple array conditions. The results are also compared to the Cramer-Rao lower bound (CRLB) and conventional beamforming. For each source case, CNNs were trained for a perfect uniform line array (ULA) and tested against data from a perfect ULA, perturbed ULAs, ULAs with missing sensors, and ULAs with muffled sensors. We show that for the single-source case, the CNNs do not offer any performance improvement relative to MUSIC at low signal-to-noise ratio (SNR). For the two-source cases, the CNNs perform better than MUSIC but only at low SNR. For the remaining array cases, the CNNs outperform MUSIC. These results indicate that the performance improvements from CNNs are highest for situations where there is signal model to data mismatch (imperfect information). This work also illustrates that the CNN estimators developed in this work exceed the CRLB and are biased estimators caused by the lack of unbiased constraint in the loss function during training of the CNNs. |
| format | Article |
| id | doaj-art-c2d4be49584c4aa18d7304c241136e6d |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-c2d4be49584c4aa18d7304c241136e6d2025-02-12T00:01:47ZengIEEEIEEE Access2169-35362025-01-0113255332554510.1109/ACCESS.2025.353899710872920Convolutional Neural Networks for Direction of Arrival Estimation Compared to Classical Estimators and BoundsChristopher J. Bell0https://orcid.org/0009-0005-6487-5038Kaushallya Adhikari1https://orcid.org/0000-0002-0706-0781Andrew Brown2https://orcid.org/0009-0004-1091-4989Department of Electrical, Computer and Biomedical Engineering, The University of Rhode Island, Kingston, RI, USADepartment of Electrical, Computer and Biomedical Engineering, The University of Rhode Island, Kingston, RI, USADepartment of Electrical, Computer and Biomedical Engineering, The University of Rhode Island, Kingston, RI, USARecently, there has been a proliferation of applied machine learning (ML) research, including the use of convolutional neural networks (CNNs) for direction of arrival (DoA) estimation. With the increasing amount of research in this area, it is important to balance the performance and computational costs of CNNs with classical methods of DoA estimation such as Multiple Signal Classification (MUSIC). We outline the performance of both methods of DoA estimation for single-source and two-source cases for multiple array conditions. The results are also compared to the Cramer-Rao lower bound (CRLB) and conventional beamforming. For each source case, CNNs were trained for a perfect uniform line array (ULA) and tested against data from a perfect ULA, perturbed ULAs, ULAs with missing sensors, and ULAs with muffled sensors. We show that for the single-source case, the CNNs do not offer any performance improvement relative to MUSIC at low signal-to-noise ratio (SNR). For the two-source cases, the CNNs perform better than MUSIC but only at low SNR. For the remaining array cases, the CNNs outperform MUSIC. These results indicate that the performance improvements from CNNs are highest for situations where there is signal model to data mismatch (imperfect information). This work also illustrates that the CNN estimators developed in this work exceed the CRLB and are biased estimators caused by the lack of unbiased constraint in the loss function during training of the CNNs.https://ieeexplore.ieee.org/document/10872920/Convolutional neural networkdirection of arrivalmultiple signal classificationCramer-Rao lower boundregressionperturbed array |
| spellingShingle | Christopher J. Bell Kaushallya Adhikari Andrew Brown Convolutional Neural Networks for Direction of Arrival Estimation Compared to Classical Estimators and Bounds IEEE Access Convolutional neural network direction of arrival multiple signal classification Cramer-Rao lower bound regression perturbed array |
| title | Convolutional Neural Networks for Direction of Arrival Estimation Compared to Classical Estimators and Bounds |
| title_full | Convolutional Neural Networks for Direction of Arrival Estimation Compared to Classical Estimators and Bounds |
| title_fullStr | Convolutional Neural Networks for Direction of Arrival Estimation Compared to Classical Estimators and Bounds |
| title_full_unstemmed | Convolutional Neural Networks for Direction of Arrival Estimation Compared to Classical Estimators and Bounds |
| title_short | Convolutional Neural Networks for Direction of Arrival Estimation Compared to Classical Estimators and Bounds |
| title_sort | convolutional neural networks for direction of arrival estimation compared to classical estimators and bounds |
| topic | Convolutional neural network direction of arrival multiple signal classification Cramer-Rao lower bound regression perturbed array |
| url | https://ieeexplore.ieee.org/document/10872920/ |
| work_keys_str_mv | AT christopherjbell convolutionalneuralnetworksfordirectionofarrivalestimationcomparedtoclassicalestimatorsandbounds AT kaushallyaadhikari convolutionalneuralnetworksfordirectionofarrivalestimationcomparedtoclassicalestimatorsandbounds AT andrewbrown convolutionalneuralnetworksfordirectionofarrivalestimationcomparedtoclassicalestimatorsandbounds |