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Implementation of a novel Fibonacci branch search optimizer for the design of the low sidelobe and deep nulling adaptive beamformer

Part of: EuMW Special issue 2019

Published online by Cambridge University Press:  07 January 2020

Haichuan Zhang Open the ORCID record for Haichuan Zhang [Opens in a new window]  and
Fangling Zeng
Haichuan Zhang*
Affiliation:
School of Electronic Countermeasures, National University of Defense Technology, Hefei, China
Fangling Zeng
Affiliation:
School of Electronic Countermeasures, National University of Defense Technology, Hefei, China
*
Author for correspondence: Haichuan Zhang, E-mail: zhanghai4258@163.com
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Abstract

In this work, we proposed an adaptive beamformer based on a novel heuristic optimization algorithm. The novel optimization technique inspired from Fibonacci sequence principle, designated as Fibonacci branch search (FBS), used new tree's branches fundamental structure and interactive searching rules to obtain the global optimal solution in the search space. The branch structure of FBS is selected using two types of multidimensional points on the basis of shortening fraction formed by Fibonacci sequence; in this mode, interactive global and local searching rules are implemented alternately to obtain the optimal solutions, avoiding stagnating in local optimum. The proposed FBS is also used here to construct an adaptive beamforming (ABF) technique as a real-time implementation to achieve near-optimal performance for its simplicity and high convergence rate, then, the performance of the FBS is compared with the five typical heuristic optimization algorithms. Simulation results demonstrate the superiority of the proposed FBS approach in locating the optimal solution with higher precision and reveal further improvement in the ABF performance.

Keywords

Fibonacci branch searchglobal optimal solutioninteractive searchingrobust adaptive beamformershortening fraction
Type
Antenna Design, Modelling and Measurements
Information
International Journal of Microwave and Wireless Technologies , Volume 12 , Special Issue 7: EuMW 2019 Special Issue , September 2020 , pp. 660 - 677
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Copyright
Copyright © Cambridge University Press and the European Microwave Association 2020

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References

Komljenovic, T, Helkey, R, Coldren, L and bowers, JE (2017). Sparse aperiodic arrays for optical beam forming and LIDAR. Optics Express 25, 2511.CrossRefGoogle ScholarPubMed
Madanayake, A, Wijenayake, C, Dansereau, DG, Gunaratne, TK, Bruton, LT and Williams, SB (2013). Multidimensional (MD) circuits and systems for emerging applications including cognitive radio, radio astronomy, robot vision and imaging. Circuits & Systems Magazine IEEE 13, 10–43.CrossRefGoogle Scholar
Bing, H and Leger, J (2008). Numerical aperture invariant focus shaping using spirally polarized beams. Optics Communications 281, 1924–1928.Google Scholar
Zaharis, ZD, Skeberis, C, Xenos, TD, Lazaridis, PI and Cosmas, J (2013). Design of a novel antenna array beamformer using neural networks trained by modified adaptive dispersion invasive weed optimization based data. IEEE Transactions on Broadcasting 59, 455–460.CrossRefGoogle Scholar
El-Keyi, A and Champagne, B (2009). Collaborative uplink transmit beamforming with robustness against channel estimation errors. IEEE Transactions on Vehicular Technology 58, 126–139.CrossRefGoogle Scholar
Hassani, A, Plata-Chaves, J, Bertrand, A and Moonen, M (2017). Multi-task wireless sensor network for joint distributed node-specific signal enhancement, LCMV beamforming and DOA estimation. IEEE Journal of Selected Topics in Signal Processing 11, 518–533.CrossRefGoogle Scholar
Roshanaei, M, Lucas, C and Mehrabian, AR (2009). Adaptive beamforming using a novel numerical optimisation algorithm. IET Microwaves, Antennas & Propagation 3, 765–773.CrossRefGoogle Scholar
Al-Ardi, EM, Shubair, RM and Al-Mualla, ME (2004). Performance evaluation of the LMS adaptive beamforming algorithm used in smart antenna systems. IEEE Midwest Symposium on Circuits & Systems.Google Scholar
Rasmussen, TK and Krink, T (2003). Improved hidden Markov model training for multiple sequence alignment by a particle swarm optimization-evolutionary algorithm hybrid. Biosystems 72, 5–17.CrossRefGoogle ScholarPubMed
Wei, C and Lu, Y (2015). Adaptive beamforming for arbitrary array by particle swarm optimization. IEEE International Conference on Computational Electromagnetics.Google Scholar
Vitale, M, Vesentini, G, Ahmad, NN and Hanzo, L (2002). Genetic algorithm assisted adaptive beamforming. Proceedings IEEE 56th Vehicular Technology Conference.CrossRefGoogle Scholar
Maina, RM, Langat, K and Kihato, PK (2013). Comparative analysis between use of particle swarm optimization and simulated annealing algorithms in beam null steering a rectangular array system. International Journal of Innovative Research & Development 2, 7.Google Scholar
Darzi, S, Tiong, SK, Islam, MT, Ismail, M and Kibria, S (2015). Optimal null steering of minimum variance distortionless response adaptive beamforming using particle swarm optimization and gravitational search algorithm. IEEE International Symposium on Telecommunication Technologies.Google Scholar
Mahmoud, KR, El-Adawy, M, Ibrahem, SMM, Bansal, R, Mahmoud, KR and Zainud-Deen, SH (2008). Performance of circular Yagi-Uda arrays for beamforming applications using particle swarm optimization algorithm. Journal of Electromagnetic Waves & Applications 22, 353–364.CrossRefGoogle Scholar
He, L and Huang, S (2017). Modified firefly algorithm based multilevel thresholding for color image segmentation. Neurocomputing 240, 152–174.CrossRefGoogle Scholar
Berryman, MJ, Allison, A and Abbott, D (2004). Optimizing genetic algorithm strategies for evolving networks. Noise in Communication. International Society for Optics and Photonics 5473, 122–131.Google Scholar
Korošec, P, Šilc, J and Filipič, B (2012). The differential ant-stigmergy algorithm. Information Sciences 192, 82–97.CrossRefGoogle Scholar
Liao, B and Chan, SC (2012). Adaptive beamforming for uniform linear arrays with unknown mutual coupling. IEEE Antennas and Wireless Propagation Letters 11, 464–467.CrossRefGoogle Scholar
Etminaniesfahani, A, Ghanbarzadeh, A and Marashi, Z (2018). Fibonacci indicator algorithm: a novel tool for complex optimization problems. Engineering Applications of Artificial Intelligence 74, 1–9.CrossRefGoogle Scholar
Subasi, M, Yildirim, N and Yildiz, B (2004). An improvement on Fibonacci search method in optimization theory. Applied Mathematics and Computation 147, 893–901.CrossRefGoogle Scholar
Yildiz, B and Karaduman, E (2003). On Fibonacci search method with k-Lucas numbers. Applied Mathematics and Computation 143, 523–531.CrossRefGoogle Scholar
Omolehin, JO, Ibiejugba, MA, Onachi, AE and Evans, DJ (2005). A Fibonacci search technique for a class of multivariable functions and ODEs. International Journal of Computer Mathematics 82, 1505–1524.CrossRefGoogle Scholar
Jayaprakasam, S, Abdul Rahim, SK, Leow, CY, Ting, TO (2017). Sidelobe reduction and capacity improvement of open-loop collaborative beamforming in wireless sensor networks. PLoS ONE 12, e0175510.CrossRefGoogle ScholarPubMed
Adorio, EP and Diliman, U (2005). Mvf-multivariate test functions library in c for unconstrained global optimization. Quezon City, Metro Manila, Philippines, pp. 100–104.Google Scholar
Molga, M and Smutnicki, C (2005). Test functions for optimization needs. Test functions for optimization needs, 101.Google Scholar
Cui, L, Li, G, Wang, X-Z, Lin, Q, Chen, J, Lu, N, Lu, J (2017). A ranking-based adaptive artificial bee colony algorithm for global numerical optimization. Information Sciences 417, 169–185.CrossRefGoogle Scholar
Zhang, C, Ning, J, Lu, S, Ouyang, D and Ding, T (2009). A novel hybrid differential evolution and particle swarm optimization algorithm for unconstrained optimization. Operations Research Letters 37, 117–122.CrossRefGoogle Scholar
Mallipeddi, R, Lie, JP, Suganthan, PN, Razul, SG and See, CMS (2011). A differential evolution approach for robust adaptive beamforming based on joint estimation of look direction and array geometry. Progress in Electromagnetics Research 2011, 381–394.CrossRefGoogle Scholar
Banerjee, S and Dwivedi, VV (2016). Performance analysis of adaptive beamforming using particle swarm optimization. 11th International Conference on Industrial and Information Systems (ICIIS). IEEE, pp. 242–246.CrossRefGoogle Scholar
Ismaiel, AM, Elsaidy, EI, Albagory, Y, Atallah, HA, Abdel-Rahman, AB and Sallam, T (2018). Performance improvement of high altitude platform using concentric circular antenna array based on particle swarm optimization. AEU-International Journal of Electronics and Communications 91, 85–90.CrossRefGoogle Scholar
Ruchi, R, Nandi, A and Basu, B (2015). Design of beam forming network for time modulated linear array with artificial bees colony algorithm. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields 28, 508–521.CrossRefGoogle Scholar
Lu, Y, Yeo, B-K (2000). Adaptive wide null steering for digital beamforming array with the complex coded genetic algorithm[C]// Phased Array Systems and Technology, 2000. Proceedings. 2000 IEEE International Conference on. IEEE.Google Scholar