Optimization Modeling of the Hybrid Antenna Array for the DoA Estimation
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Optimization Modeling of the Hybrid Antenna Array for the DoA Estimation

Authors: Somayeh Komeylian


The direction of arrival (DoA) estimation is the crucial aspect of the radar technologies for detecting and dividing several signal sources. In this scenario, the antenna array output modeling involves numerous parameters including noise samples, signal waveform, signal directions, signal number, and signal to noise ratio (SNR), and thereby the methods of the DoA estimation rely heavily on the generalization characteristic for establishing a large number of the training data sets. Hence, we have analogously represented the two different optimization models of the DoA estimation; (1) the implementation of the decision directed acyclic graph (DDAG) for the multiclass least-squares support vector machine (LS-SVM), and (2) the optimization method of the deep neural network (DNN) radial basis function (RBF). We have rigorously verified that the LS-SVM DDAG algorithm is capable of accurately classifying DoAs for the three classes. However, the accuracy and robustness of the DoA estimation are still highly sensitive to technological imperfections of the antenna arrays such as non-ideal array design and manufacture, array implementation, mutual coupling effect, and background radiation and thereby the method may fail in representing high precision for the DoA estimation. Therefore, this work has a further contribution on developing the DNN-RBF model for the DoA estimation for overcoming the limitations of the non-parametric and data-driven methods in terms of array imperfection and generalization. The numerical results of implementing the DNN-RBF model have confirmed the better performance of the DoA estimation compared with the LS-SVM algorithm. Consequently, we have analogously evaluated the performance of utilizing the two aforementioned optimization methods for the DoA estimation using the concept of the mean squared error (MSE).

Keywords: DoA estimation, adaptive antenna array, Deep Neural Network, LS-SVM optimization model, radial basis function, MSE.

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[1] Bindong Gao, Fangzheng Zhang, Ermao Zhao, Daocheng Zhang, and Shilong Pan, “High-resolution phased array radar imaging by photonics-based broadband digital beamforming,” Optics Express 13194 27, No. 9, 2019.
[2] Najam-Us Saqib, Imdad Khan, “A Hybrid Antenna Array Design for 3-D Direction of Arrival Estimation,” PLoS ONE 10(3): e0118914. doi:10.1371/journal, March 19, 2015.
[3] Sangwook Kim, Swathi Kavuri, and Minho Lee, “Deep Network with Support Vector Machines,” M. Lee et al. (Eds.): ICONIP 2013, Part I, LNCS 8226, pp. 458–465, Springer-Verlag Berlin Heidelberg 2013.
[4] Gonzalo Acuña and Millaray Curilem, “Comparison of Neural Networks and Support Vector Machine Dynamic Models for State Estimation in Semiautogenous Mills,” A. Hernández Aguirre et al. (Eds.): MICAI 2009, LNAI 5845, pp. 478–487, Springer-Verlag Berlin Heidelberg 2009.
[5] Yue Wu, Hui Wang, Biaobiao Zhang, and K.-L. Du, “Using Radial Basis Function Networks for Function Approximation and Classification,” International Scholarly Research Network, ISRN Applied Mathematics, Article ID 324194, 34 pages, doi:10.5402/2012/324194, 2012.
[6] JAK Suykens, J De Brabanter, L Lukas, J Vandewalle, “Neurocomputing, Weighted least squares support vector machines: robustness and sparse approximation,” Elsevier, 2002.
[7] A. W. Jayawardena, D. A. K. Fernando & M. C. Zhou, “Comparison of Multilayer Perceptron and Radial Basis Function networks as tools for flood forecasting,” Destructive Water: Water-Caused Natural Disasters, their Abatement and Control (Proceedings of the Conference held at Anaheim, California, June 1996). IAHS Publ. no. 239, 1997.
[8] J.A.K. Suykens, and J. Vandewalle, “Least Squares Support Vector Machine Classifiers,” 1999 Kluwer Academic Publishers. Printed in the Netherlands, 1999.
[9] Marija Agatonović, Zoran Stanković, Bratislav Milovanović, and Nebojša Dončov, “DOA Estimation using Radial Basis Function Neural Networks as Uniform Circular Antenna Array Signal Processor,” 2011 IEEE.
[10] Yu. B. Nechaev, I.W. Peshkov, N.A. Fortunova, “Cylindrical antenna array development and measurements for DOA-estimation applications,” 2017 XI International Conference on Antenna Theory and Techniques (ICATT), 2017.
[11] Slavko RUPCIC, Vanja MANDRIC, and Drago ZAGAR, “Reduction of Sidelobes by Nonuniform Elements Spacing of a Spherical Antenna Array,” Radio Engineering, Vol. 20, No. 1, April 2011.
[12] Tony van Gestel, Johan A.K. Suykens, Bart Baesens, Stijn Viaene, Jan Vanthienen, Guido Dedene, Bart de Moor and Joos Vandewalle, “Benchmarking Least Squares Support Vector Machine Classifiers,” 2004 Kluwer Academic Publishers, Manufactured in The Netherlands, 2004.
[13] Soodabeh Darzi, Tiong Sieh Kiong, Mohammad Tariqul Islam, Mahamod Ismail, Salehin Kibria, and Balasem Salem, “Null Steering of Adaptive Beam forming Using Linear Constraint Minimum Variance Assisted by Particle Swarm Optimization, Dynamic Mutated Artificial Immune System, and Gravitational Search Algorithm,” Hindawi Publishing Corporation, the Scientific World Journal Volume 2014, Article ID 724639, 2014.
[14] Najam-Us Saqib, Imdad Khan, “A Hybrid Antenna Array Design for 3-D Direction of Arrival Estimation,” PLOS ONE | DOI:10.1371/journal. pone.0118914, 2015.