A Multiclass Support Vector Machine Based Direction-of-Arrival Estimation Technique using Spherical Antenna Array with Undefined Mutual Coupling
Abstract
In antenna array signal processing, estimating the direction-of-arrival (DoA) remains a challenge and basic problem. In this paper, a DoA estimation technique using support vector machine (SVM) classification is developed using spherical antenna array (SAA). The source signal impinging on SAA is decomposed using spherical harmonics (SH). Both magnitude and phase features are computed from the decomposed SH signals. The magnitude and phase features are classified into DoA classes using multi-class SVM (MC-SVM) algorithm. Due to the deterministic and non-probabilistic nature of SVM algorithm, it exhibits high computational speed and less complex than the neural network-dependent learning algorithms. Numerical experiments and experimental measured data (generally accepted ground to test any method) are used to evaluate the performance of the proposed technique. The developed algorithm exhibit high level of robustness at different signal-to-noise ratios (SNR) in the estimation of DoA. Root mean square error (RMSE) performance metrics is employed in the analysis of the proposed method against the state-of-the-art. The results obtained are motivating enough for the deployment of the proposed algorithm in practical scenarios.
Keywords
References
O. J. Famoriji, and T. Shongwe, “Multi-source DoA estimation of EM waves impinging spherical antenna array with unknown mutual coupling using relative signal pressure based multiple signal classification approach,” IEEE Access, vol. 12, pp. 103793 – 103803, October, 2022.
A. Fahim, P. N. Samarasinghe, and T. D. Abhayapala, “PSD estimation and source separation in a noisy reverberant environment using a spherical microphone array,” IEEE/ACM Trans. Audio Speech Lang. Process., vol. 26, no. 9, pp. 1594–1607, Sep. 2018.
F. Asano, M. Goto, K. Itou, and H. Asoh, “Real-time sound source localization and separation system and its application to automatic speech recognition,” in Proc. 7th Eur. Conf. Speech Commun. Technol., 2001, pp. 1013–1016.
S. Adavanne, A. Politis, J. Nikunen, and T. Virtanen, “Sound event localization and detection of overlapping sources using convolutional recurrent neural networks,” IEEE J. Sel. Topics Signal Process., vol. 13, no. 1, pp. 34–48, Mar. 2019.
O. J. Famoriji, and T. Shongwe, “Subspace Pseudointensity Vectors Approach for DoA Estimation Using Spherical Antenna Array in the Presence of Unknown Mutual Coupling,” Applied Sciences, vol. 2022, 12, 10099, pp. 1-14. https://doi.org/10.3390/ app121910099.
P. Kumar, C. Kumar, S. Kumar, and V. Srinivasan, “Active spherical phased array design for satellite payload data transmission,” IEEE Trans. Antennas Propagat., vol. 63, no. 11 pp. 4783–4791, Nov. 2015.
P. Knott, “Design and experimental results of a spherical antenna array for a conformal array demonstrate,” in Proceeding 2007 2nd International ITG Conference on Antennas, pp. 1-4, Munich, Germany.
O. J. Famoriji, O. Y. Ogundepo, X. Qi, “An intelligent deep learning-based direction-of-arrival estimation scheme using spherical antenna array with unknown mutual coupling,” IEEE Access, vol.8, pp. 179259–179271, Sept., 2020.
L. Kumar, G. Bi, and R. M. Hegde, “The spherical harmonics root-music,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Mar. 2016, pp. 3046–3050.
H. Sun, E. Mabande, K. Kowalczyk, and W. Kellermann, “Joint DOA and TDOA estimation for 3D localization of reflective surfaces using eigenbeam MVDR and spherical microphone arrays,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., May 2011, pp. 113–116.
O. J. Famoriji, and T. Shongwe, “Critical review of basic methods on DoA estimation of EM waves impinging a spherical antenna array,” Electronics, vol. 11, Article ID 208, pp. 1-25, January, 2022.
E. Mabande, H. Sun, K. Kowalczyk, and W. Kellermann, “Comparison of subspace-based and steered beamformer-based reflection localization methods,” in Proc. Eur. Signal Process. Conf., Aug. 2011, pp. 146–150.
S. Adavanne, A. Politis, and T. Virtanen, “Direction of arrival estimation for multiple sound sources using convolutional recurrent neural network,” in Proc. Eur. Signal Process. Conf., Sep. 2018, pp. 1462– 1466.
B. Laufer, R. Talmon, and S. Gannot, “Semi-supervised sound source localization based on manifold regularization,” vol. 24, no. 8, pp. 1393–1407, Aug. 2016.
O. J. Famoriji, T. Shongwe, “Direction-of-arrival estimation of electromagnetic wave impinging on spherical antenna array in the presence of mutual coupling using a multiple signal classification method,” Electronics, vol. 10, no. 2651, pp. 1-15, 2021.
S. Chakrabarty and E. A. P. Habets, “Broadband DOA estimation using convolutional neural networks trained with noise signals,” in Proc. IEEE Workshop Appl. Signal Process. Audio Acoust., Oct. 2017, pp. 136–140.
R. Takeda and K. Komatani, “Sound source localization based on deep neural networks with directional activate function exploiting phase information,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Mar. 2016, pp. 405–409.
O. J. Famoriji, T. Shongwe, “Source localization of EM waves in the near-field of spherical antenna array in the presence of unknown mutual coupling,” Wireless Communications and Mobile Computing, vol. 2021, Article ID 3237219, pp. 1-14, 2021.
D. R. Don and I. E. Iacob, “DCSVM: fast multi-class classification using support vector machines,” CoRR, vol. abs/1810.09828, 2018. [Online]. Available: http://arxiv.org/abs/1810.09828
S. Yadav, M. Wajid, and M. Usman, Support Vector Machine-Based Direction of Arrival Estimation with Uniform Linear Array. Singapore: Springer Singapore, 2020, pp. 253–264.
A. J. van der Veen, P. B. Ober, and E. F. Deprettere, “Azimuth and elevation computation in high resolution doa estimation,” IEEE Transactions on Signal Processing, vol. 40, no. 7, pp. 1828–1832, 1992.
P. Dwivedi, G. Routray, and R. M. Hegde, “DoA estimation using multiclass-SVM in spherical harmonics domain,” in 2022 IEEE International Conference on Signal Processing Communications (SPCOM), 2022, pp. 1-5.
B. Rafaely, Fundamentals of spherical array processing. Springer, 2015, vol. 8.
Z. Xiaofei, L. Wen, S. Ying, Z. Ruina, and X. Dazhuan, “A novel doa estimation algorithm based on eigen space,” in 2007 International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, 2007, pp. 551–554.
O. Nadiri and B. Rafaely, “Localization of multiple speakers under high reverberation using a spherical microphone array and the directpath dominance test,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 22, no. 10, pp. 1494–1505, 2014.
O. J. Famoriji, and T. Shongwe, “Electromagnetic machine learning for estimation and mitigation of mutual coupling in strongly coupled arrays,” ICT Express, https://doi.org/10.1016/j.icte.2021.10.009
Refbacks
- There are currently no refbacks.
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272
This work is licensed under a Creative Commons Attribution 4.0 International License.