Vol. 88
Latest Volume
All Volumes
PIERB 117 [2026] PIERB 116 [2026] PIERB 115 [2025] PIERB 114 [2025] PIERB 113 [2025] PIERB 112 [2025] PIERB 111 [2025] PIERB 110 [2025] PIERB 109 [2024] PIERB 108 [2024] PIERB 107 [2024] PIERB 106 [2024] PIERB 105 [2024] PIERB 104 [2024] PIERB 103 [2023] PIERB 102 [2023] PIERB 101 [2023] PIERB 100 [2023] PIERB 99 [2023] PIERB 98 [2023] PIERB 97 [2022] PIERB 96 [2022] PIERB 95 [2022] PIERB 94 [2021] PIERB 93 [2021] PIERB 92 [2021] PIERB 91 [2021] PIERB 90 [2021] PIERB 89 [2020] PIERB 88 [2020] PIERB 87 [2020] PIERB 86 [2020] PIERB 85 [2019] PIERB 84 [2019] PIERB 83 [2019] PIERB 82 [2018] PIERB 81 [2018] PIERB 80 [2018] PIERB 79 [2017] PIERB 78 [2017] PIERB 77 [2017] PIERB 76 [2017] PIERB 75 [2017] PIERB 74 [2017] PIERB 73 [2017] PIERB 72 [2017] PIERB 71 [2016] PIERB 70 [2016] PIERB 69 [2016] PIERB 68 [2016] PIERB 67 [2016] PIERB 66 [2016] PIERB 65 [2016] PIERB 64 [2015] PIERB 63 [2015] PIERB 62 [2015] PIERB 61 [2014] PIERB 60 [2014] PIERB 59 [2014] PIERB 58 [2014] PIERB 57 [2014] PIERB 56 [2013] PIERB 55 [2013] PIERB 54 [2013] PIERB 53 [2013] PIERB 52 [2013] PIERB 51 [2013] PIERB 50 [2013] PIERB 49 [2013] PIERB 48 [2013] PIERB 47 [2013] PIERB 46 [2013] PIERB 45 [2012] PIERB 44 [2012] PIERB 43 [2012] PIERB 42 [2012] PIERB 41 [2012] PIERB 40 [2012] PIERB 39 [2012] PIERB 38 [2012] PIERB 37 [2012] PIERB 36 [2012] PIERB 35 [2011] PIERB 34 [2011] PIERB 33 [2011] PIERB 32 [2011] PIERB 31 [2011] PIERB 30 [2011] PIERB 29 [2011] PIERB 28 [2011] PIERB 27 [2011] PIERB 26 [2010] PIERB 25 [2010] PIERB 24 [2010] PIERB 23 [2010] PIERB 22 [2010] PIERB 21 [2010] PIERB 20 [2010] PIERB 19 [2010] PIERB 18 [2009] PIERB 17 [2009] PIERB 16 [2009] PIERB 15 [2009] PIERB 14 [2009] PIERB 13 [2009] PIERB 12 [2009] PIERB 11 [2009] PIERB 10 [2008] PIERB 9 [2008] PIERB 8 [2008] PIERB 7 [2008] PIERB 6 [2008] PIERB 5 [2008] PIERB 4 [2008] PIERB 3 [2008] PIERB 2 [2008] PIERB 1 [2008]
2020-09-03
A Beamformer Design Based on Fibonacci Branch Search
By
Progress In Electromagnetics Research B, Vol. 88, 73-95, 2020
Abstract
An approach towards beamforming for a uniform linear array (ULA) based on a novel optimization algorithm, designated as Fibonacci branch search (FBS) is presented in this paper. The proposed FBS search strategy was inspired from Fibonacci sequence principle and uses a fundamental branch structure and interactive searching rules to obtain the global optimal solution in the search space. The structure of FBS is established by two types of multidimensional points on the basis of shortening fraction formed by the Fibonacci sequence, and in this mode, interactive global searching and local optimization rules are implemented alternately to reach global optima, avoiding stagnating in local optimum. At the same time, the rigorous mathematical proof for the accessibility and convergence of FBS towards the global optimum is presented to further verify the validity of our theory and support our claim.Taking advantage of the global search ability and high convergence rate of this technique, a robust adaptive beamformer technique is also constructed here by FBS as a real time implementation to improve the beamforming performance by preventing the loss of optimal trajectory. The performance of the FBS is compared with five typical heuristic optimization algorithms, and the reported simulation results demonstrate the superiority of the proposed FBS algorithm in locating the optimal solution with higher precision and reveal the further improvement in adaptive beamforming performance.
Citation
Tianbao Dong, Haichuan Zhang, and Fangling Zeng, "A Beamformer Design Based on Fibonacci Branch Search," Progress In Electromagnetics Research B, Vol. 88, 73-95, 2020.
doi:10.2528/PIERB20033103
References

1. Huang, X., L. Bai, I. Vinogradov, and E. Peers, "Adaptive beamforming for array signal processing in aeroacoustic measurements," Journal of the Acoustical Society of America, Vol. 131, No. 3, 2152-2161, 2012.        Google Scholar

2. Daneshmand, S., N. Sokhandan, M. Zaeriamirani, and G. Lachapelle, "Precise calibration of a GNSS antenna array for adaptive beamforming applications," Sensors, Vol. 14, No. 6, 9669, 2014.        Google Scholar

3. Leight, J. and B. Toland, "Photonic beamforming technologies for advanced military and commercial SATCOM antennas," Aerospace Conference, 1999.        Google Scholar

4. Zhao, H., B. Lian, and J. Feng, "Space-time adaptive processing for GPS anti-jamming receiver," Physics Procedia, Vol. 33, No. 1, 1060-1067, 2012.        Google Scholar

5. Synnevag, J. F., A. Austeng, and S. Holm, "Adaptive beamforming applied to medical ultrasound imaging," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, Vol. 54, No. 8, 1606-1613, 2007.        Google Scholar

6. Khamy, S. E. E. and A. M. Gaballa, "Adaptive arrays for MC-CDMA using the MSINR guided multimodulus algorithm," 2008 National Radio Science Conference, 2008.        Google Scholar

7. Chen, H. W. and J. W. Zhao, "Wideband MVDR beamforming for acoustic vector sensor linear array," IEE Proceedings — Radar, Sonar and Navigation, Vol. 151, No. 3, 158-162, 2004.        Google Scholar

8. Mu, P., L. Dan, and Q. Yin, "A robust MVDR beamforming based on covariance matrix reconstruction," International Conference on Graphic & Image Processing, 2011.        Google Scholar

9. Sinha, P., A. D. George, and K. Kim, "Parallel algorithms for robust broadband MVDR beamforming," Journal of Computational Acoustics, Vol. 10, No. 1, 69-96, 2002.        Google Scholar

10. Shahbazpanahi, S., A. B. Gershman, and Z.-Q. Luo, "Robust adaptive beamforming using worst-case SINR optimization: A new diagonal loading-type solution for general-rank signal models," Proceedings, IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003.        Google Scholar

11. Mozaffarzadeh, M., A. Mahloojifar, M. Orooji, K. Kratkiewicz, S. Adabi, and M. Nasiriavanaki, "Linear-array photoacoustic imaging using minimum variance-based delay multiply and sum adaptive beamforming algorithm," Journal of Biomedical Optics, Vol. 23, No. 2, 026002, 2018.        Google Scholar

12. Shahab, S. N., A. R. Zainun, H. A. Ali, M. Hojabri, and H. Nurul, "MVDR algorithm based linear antenna array performance assessment for adaptive beamforming application," Journal of Engineering Science and Technology, Vol. 12, No. 5, 1366-1385, 2017.        Google Scholar

13. Wei, C. and Y. Lu, "Adaptive beamforming for arbitrary array by particle swarm optimization," IEEE International Conference on Computational Electromagnetics, 2015.        Google Scholar

14. Vitale, M., G. Vesentini, N. N. Ahmad, and L. Hanzo, "Genetic algorithm assisted adaptive beamforming," Proceedings IEEE 56th Vehicular Technology Conference, 2002.        Google Scholar

15. He, L. and S. Huang, "Modified firefly algorithm based multilevel thresholding for color image segmentation," Neurocomputing, Vol. 240, 152-174, 2017.        Google Scholar

16. Sun, K., S. Mou, J. Qiu, T. Wang, and H. Gao, "Adaptive fuzzy control for non-triangular structural stochastic switched nonlinear systems with full state constraints," IEEE Transactions on Fuzzy Systems, Vol. 27, No. 8, 1587-1601, 2018.        Google Scholar

17. Qiu, J., K. Sun, T. Wang, and H. Gao, "Observer-based fuzzy adaptive event-triggered control for pure-feedback nonlinear systems with prescribed performance," IEEE Transactions on Fuzzy Systems, Vol. 27, No. 11, 2152-2162, 2019.        Google Scholar

18. Liao, B. and S. C. Chan, "Adaptive beamforming for uniform linear arrays with unknown mutual coupling," IEEE Antennas and Wireless Propagation Letters, Vol. 11, 464-467, 2012.        Google Scholar

19. Etminaniesfahani, A., A. Ghanbarzadeh, and Z. Marashi, "Fibonacci indicator algorithm: A novel tool for complex optimization problems," Engineering Applications of Artificial Intelligence, Vol. 74, 1-9, 2018.        Google Scholar

20. Subasi, M., N. Yildirim, and B. Yildiz, "An improvement on Fibonacci search method in optimization theory," Applied Mathematics and Computation, Vol. 147, 893-901, 2004.        Google Scholar

21. Yildiz, B. and E. Karaduman, "On Fibonacci search method with k-Lucas numbers," Applied Mathematics and Computation, Vol. 143, 523-531, 2003.        Google Scholar

22. Omolehin, J. O., M. A. Ibiejugba, A. E. Onachi, and D. J. Evans, "A Fibonacci Search technique for a class of multivariable functions and ODEs," International Journal of Computer Mathematics, Vol. 82, 1505-1524, 2005.        Google Scholar

23. Ramaprabha, R., M. Balaji, and B. L. Mathur, "Maximum power point tracking of partially shaded solar PV system using modified Fibonacci search method with fuzzy controller," InternationaJournal of Electrical Power & Energy Systems, Vol. 43, 754-765, 2012.        Google Scholar

24. Wang, X., D. J. Lyu, Y. Dong, et al. "Cutting parameters multi-scheme optimization based on Fibonacci tree optimization algorithm," Control and Decision, Vol. 33, 1373-1381, 2018.        Google Scholar

25. Kaid Omar, O., F. Debbat, and A. Boudghene Stambouli, "Null steering beamformer using hybrid algorithm based on Honey Bees Mating Optimisation and Tabu Search in adaptive antenna array," Progress In Electromagnetics Research C, Vol. 32, 65-80, 2012.        Google Scholar

26. Ng, C. K. and D. Li, "Test problem generator for unconstrained global optimization," Computers & Operations Research, Vol. 51, No. 51, 338-349, 2014.        Google Scholar

27. Yang, Y. and Y. Shang, "A new filled function method for unconstrained global optimization," Mathematical Problems in Engineering, Vol. 8, No. 1, 501-512, 2010.        Google Scholar

28. Saeed, S., H. C. Ong, and S. Sathasivam, "Self-adaptive single objective hybrid algorithm for unconstrained and constrained test functions: An application of optimization algorithm," Arabian Journal for Science and Engineering, Vol. 44, No. 4, 3497-3513, 2019.        Google Scholar

29. Mallipeddi, R., J. P. Lie, P. N. Suganthan, S. G. Razul, and C. M. S. See, "A differential evolution approach for robust adaptive beamforming based on joint estimation of look direction and array geometry," Progress In Electromagnetics Research, Vol. 119, 381-394, 2011.        Google Scholar

30. Banerjee, S. and V. V. Dwivedi, "Performance analysis of adaptive beamforming using particle swarm optimization," 11th International Conference on Industrial and Information Systems (ICIIS), 242-246, IEEE, 2016.        Google Scholar

31. Ismaiel, A. M., E. Elsaidy, and Y. Albagory, "Performance improvement of high altitude platform using concentric circular antenna array based on particle swarm optimization," AEU --- International Journal of Electronics and Communications, Vol. 91, 85-90, 2018.        Google Scholar

32. Ruchi, R., A. Nandi, and B. Basu, "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, Vol. 28, No. 5, 508-521, 2015.        Google Scholar

33. Yeo, B. K. and Y. Lu, "Adaptive array digital beamforming using complex-coded particle swarm optimization-genetic algorithm," Microwave Conference, 2006.        Google Scholar