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Optimized hyper beamforming of receiving linear antenna arrays using Firefly algorithm

Published online by Cambridge University Press:  14 October 2013

Gopi Ram ,
Durbadal Mandal ,
Rajib Kar  and
Sakti Prasad Ghoshal
Gopi Ram
Affiliation:
Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur, India
Durbadal Mandal*
Affiliation:
Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur, India
Rajib Kar
Affiliation:
Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur, India
Sakti Prasad Ghoshal
Affiliation:
Department of Electrical Engineering, National Institute of Technology, Durgapur, India
*
Corresponding author: D. Mandal Email: durbadal.bittu@gmail.com
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Abstract

In this paper, an optimized hyper beamforming method is presented based on a hyper beam exponent parameter for receiving linear antenna arrays using a new meta-heuristic search method based on the Firefly algorithm (FFA). A hyper beam is derived from the sum and difference beam patterns of the array, each raised to the power of a hyper beam exponent parameter. As compared to the conventional hyper beamforming of the linear antenna array, FFA applied to the hyper beam of the same array can achieve much more reduction in sidelobe level (SLL) and improved first null beam width (FNBW), keeping the same value of the hyper beam exponent. As compared to the uniformly excited linear antenna array with inter-element spacing of λ/2, conventional non-optimized hyper beamforming and optimal hyper beamforming of the same obtained by real-coded genetic algorithm, particle swarm optimization and Differential evolution, FFA applied to the hyper beam of the same array can achieve much greater reduction in SLL and same or less FNBW, keeping the same value of the hyper beam exponent parameter. The whole experiment has been performed for 10-, 14-, and 20-element linear antenna arrays.

Keywords

Hyper beamLinear antenna arraysRGAPSODEFFASLLFNBW

Information

Type
Research Paper
Information
International Journal of Microwave and Wireless Technologies , Volume 6 , Issue 2 , April 2014 , pp. 181 - 194
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2013 

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References

REFERENCES

[1]Schlieter, H.; Eigenbrod, H.: Method for the formation of radiated beams in direction finder systems, Patent US 6021096 A, February 1, 2000.Google Scholar
[2]Anita, V.; Sri Jaya Lakshmi, S.; Sreedevi, I.; Khan, H.; Sarat Kumar, K.; Ramakrishna, P.: An adaptive processing of linear array for target detection improve. Int. J. Comput. Appl. (0975–8887), 42 (4) (2012), 33–36.Google Scholar
[3]Isernia, T.; Ares Pena, F.J.; Bucci, O.M.; Durso, M.; Gomez, J.F.; Rodriguez, J.A.: A hybrid approach for the optimal synthesis of pencil beams through array antennas. IEEE Trans. Antennas Propag., 52 (11) (2004), 2912–2918.Google Scholar
[4]Walkar, R.S.: Bearing accuracy and resolution bound of high-resolution beam formers, in Proc. IEEE ICASSP ‘85, Tampa, FL.Google Scholar
[5]Takao, K.; Komiyama, K.: An adaptive antenna array under directional constraint. IEEE Trans. Antennas Propag., AI 24 (1976), 662–669.Google Scholar
[6]Schilter, H.: Method for three-dimensional beam forming in direction finding systems, Patent US 6178140, January 23, 2001.Google Scholar
[7]Balanis, C.A.: Antenna Theory Analysis and Design. John Wiley & Sons, New York, 1997.Google Scholar
[8]Krous, J.D.: Antenna. Mc GRAW-HILL, New York, 1950.Google Scholar
[9]Mailloux, R.J.: Phased array architecture for millimetric active arrays, IEEE Antennas Propag. Soc. Newslett., February 1986, 28, 4–7.Google Scholar
[10]Schrank, H.E.: Low sidelobe phased array antennas. IEEE Antennas Propag. Soc. Newslett., 25 (2) (1983), 4–9.Google Scholar
[11]Applebaum, S.P.; Chapman, D.J.: Adaptive arrays with main beam constraints. IEEE Trans. Antennas Propag., AI-24 (1976), 650–662.Google Scholar
[12]Chen, S.: IIR model identification using batch-recursive adaptive simulated annealing algorithm, in 6th Annual Chinese Automation and Computer Science Conf., 2000, 151–155.Google Scholar
[13]Haupt, R.L.: Phase-only adaptive nulling with a genetic algorithm. IEEE Trans. Antennas Propag., 45 (6) (1997), 1009–1015.CrossRefGoogle Scholar
[14]Haupt, R.L.; Werner, D.H.: Genetic Algorithms in Electromagnetics. IEEE Press, Wiley–Interscience, John Wiley & Sons, Inc., Publication, USA, 2007.Google Scholar
[15]Chung, Y.C.; Haupt, R.L.: Adaptive nulling with spherical arrays using a genetic algorithm. Proc. IEEE AP-S Int. Symp. Digest, 3 (1999), 2000–2003.Google Scholar
[16]Hardel, G.R.; Yalapragada, N.T.; Mandal, D.; Bhattacharjee, A.K.: Introducing dipper nulls in time modulated linear symmetric antenna array using real coded genetic algorithm, in Symp. Computers and Informatics, March 2011, 249–254.CrossRefGoogle Scholar
[17]Eberhart, R.; Shi, Y.: Comparison between genetic algorithm and particle swarm optimization, in Evolutionary Programming VII, Springer, 1998, 611–616.Google Scholar
[18]Kennedy, J.; Eberhart, R.: Particle swarm optimization, in Proc. IEEE Int. Conf. Neural Network, vol. 4, 1995, 1942–1948.Google Scholar
[19]Mandal, D.; Yallaparagada, N.T.; Ghoshal, S.P.; Bhattacharjee, A.K.: Wide null control of linear antenna arrays using particle swarm optimization, in IEEE INDICON, Kolkata, India, December 2010, 1–4.Google Scholar
[20]Hao, Z.F.; Guo, G.H.; Huang, H.: A particle swarm optimization algorithm with differential evolution, in Int. Conf. Machine Learning and Cybernetics, vol. 2 (2007), 1031–1035.Google Scholar
[21]Luitel, B.; Venayagamoorthy, G.K.: Differential evolution particle swarm optimization for digital filter design, in IEEE Congress on Evolutionary Computation, CEC, 2008, 3954–3961.CrossRefGoogle Scholar
[22]Eberhart, R.C.; Shi, Y.: Particle swarm optimization: developments, applications and resources, evolutionary computation, in Proc. Congress on Evolutionary Computation, 2001, 81–86.Google Scholar
[23]Liang, J.J.; Qin, A.K.; Suganthan, P.N.; Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput., 10 (3) (2006), 281–295.Google Scholar
[24]Luitel, B.; Venayagamoorthy, G.K.: Particle swarm optimization with quantum infusion for system identification. Eng. Appl. Artif. Intell., 23 (2010), 635–649.CrossRefGoogle Scholar
[25]Panda, G.; Mohanty, D.; Majhi, B.; Sahoo, G.: Identification of nonlinear systems using particle swarm optimization technique, in IEEE Congress on Evolutionary Computation, 2007, 3253–3257.CrossRefGoogle Scholar
[26]Durmus, B.; Gun, A.: Parameter identification using particle swarm optimization, in 6th Int. Advanced Technologies Symp., IATS'11, Turkey, May 2011, 188–192.Google Scholar
[27]Van den Bergh, F.; Engelbrecht, A.P.: Cooperative learning in neural network using particle swarm optimizers. South African Comput. J., 26 (2000), 84–90.Google Scholar
[28]Storn, R.; Price, K.: Differential evolution- a simple and efficient adaptive scheme for global optimization over continuous spaces, Technical Report, International Computer Science Institute, Berkley, TR-95-012, 1995.Google Scholar
[29]Storn, R.; Price, K.V.: Minimizing the real functions of the ICEC 1996 contest by differential evolution, in Proc. 1996 IEEE Int. Conf. Evolutionary Computation, Nagoya, Japan, IEEE Press, New York, 1996, 842–844.Google Scholar
[30]Price, K.; Storn, R.; Lampinen, J.: Differential evolution – A Practical Approach to Global Optimization. Springer, Berlin, 2005.Google Scholar
[31]Das, S.; Mandal, D.; Kar, R.; Ghoshal, S.P.: Application of differential evolution with best of random mutation strategy on asymetric location only synthesis of broadside circular antenna array, in SEMCCO 2012, Bhubaneswar, Odisha, India, December 2012.Google Scholar
[32]Liu, J.; Lampinen, J.; Matousek, R.; Osmera, P.: Adaptive parameter control of differential evolution, in Proc. Mendel, 8-th Int. Conf. Soft Computing, 2002, 19–26.Google Scholar
[33]Lin, C.; Quing, A.: Synthesis of unequally spaced antenna arrays by a new differential evolution algorithm. Int. J. Commun. Netw. Inf. Secur. (IJCNIS), 1 (1) (2009), 20–25.Google Scholar
[34]Roscca, P.; Oliveri, G.; Massa, A.: Differential evolution as applied to electromagnetics. IEEE Antennas Propag. Mag., 53 (1) (2011), 38–49.Google Scholar
[35]Kurup, D.G.; Himdi, M.; Rydberg, A.: Synthesis of uniform amplitude unequally spaced antenna arrays using the differential evolution algorithm. IEEE Trans. Antennas Propag., 51 (9) (2003), 2210–2217.Google Scholar
[36]Lin, C.; Qing, A.; Feng, Q.: Synthesis of unequally spaced antenna arrays by using differential evolution. IEEE Trans. Antennas Propag., 58 (2010), 2553–2561.Google Scholar
[37]Zhang, X.; Liu, S.: Differential evolution without the scale factor F. Acta Eelctron. Sin., 36 (2009), 1318–1323.Google Scholar
[38]Qin, A.K.; Huang, V.L.; Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput., 13 (2) (2009), 398–417.Google Scholar
[39]Mandal, S.; Ghoshal, S.P.; Kar, R.; Mandal, D.: Differential evolution with wavelet mutation in digital FIR filter design. J. Optim. Theory Appl., 155 (1) (2012), 315–324.CrossRefGoogle Scholar
[40]Yang, X.S.: Firefly algorithms for multimodal optimization, In Proc. 5th Int. Conf. Stochastic Algorithms: Foundations and Applications, SAGA 2009, LNCS-Springer, vol. 5792, 2009, 169–178.Google Scholar
[41]Basu, B.; Mahanti, G.K.: Firefly and artificial bees colony algorithm for synthesis of scanned and broadside linear array antenna. Prog. Electromagn. Res. B, 32 (2011), 169–190.Google Scholar
[42]Yang, X.S.; Hosseini, S.S.; Gandomi, A.H.: Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl. Soft Comput., 12 (3) (2012), 1180–1186.Google Scholar
[43]Yang, X.S.; Deb, S.: Eagle strategy using Levy walk and firefly algorithms for stochastic optimization, Nature Inspired Cooperative Strategies for Optimization (NICSO). Stud. Comput. Intell., 284 (2010), 101–111.Google Scholar
[44]Yang, X.S.: Multiobjective firefly algorithm for continuous optimization. Eng. Comput., 29 (2) (2013), 175–184.Google Scholar