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Synthesis of nonuniformly spaced linear array of parallel and collinear dipole with minimum standing wave ratio using evolutionary optimization techniques

Published online by Cambridge University Press:  12 May 2011

Banani Basu*
Affiliation:
Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur, Durgapur 713209, India. Phone: +91-9332303363.
*
Corresponding author: B. Basu Email: [email protected]

Abstract

In this paper, the author proposes a method based on two recent evolutionary algorithms (EAS): particle swarm optimization (PSO) and differential evolution (DE) to design nonuniformly placed linear arrays of half-wavelength long dipoles. The objective of the work is to generate pencil beam in horizontal (for parallel array) and vertical (for collinear array) plane with minimum standing wave ratio (SWR) and fixed side lobe level (SLL). Dynamic range ratio (DRR) of current amplitude distribution is kept at a fixed value. Two different examples have been presented having different array alignments. For both the configurations parallel and collinear, the excitation distribution and geometry of individual array elements are perturbed to accomplish the designing goal. Coupling effect between the elements is analyzed using induced electromotive force (EMF) method and minimized in terms of SWR. Numerical results obtained from both the algorithms are statistically compared to present a comprehensive overview. Beside this, the article also efficiently computes the trade-off curves between SLL, beam width, and number of array elements for nonuniformly spaced parallel array. It featured the average element spacing versus SWR curve for nonuniformly separated arrays. Furthermore, minimum achievable SLL performances of uniformly and nonuniformly spaced parallel arrays are compared for same average spacing in the proposed work.

Type
Research Papers
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2011

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References

REFERENCES

[1]Unz, H.: Linear arrays with arbitrarily distributed elements. IRE Trans. Antennas Propag., 8 (1960), 222223.CrossRefGoogle Scholar
[2]Skolnik, M.I.; Sherman, J.W.; Nemhauser, G.: Dynamic programming applied to unequally spaced arrays. IEEE Trans. Antennas Propag., 12 (1964), 3543.CrossRefGoogle Scholar
[3]Mailloux, R.J.; Cohen, E.: Statistically thinned arrays with quantized element weights. IEEE Trans. Antennas Propag., 39 (1991), 436447.CrossRefGoogle Scholar
[4]Haupt, R.L.: Thinned arrays using genetic algorithms. IEEE Trans. Antennas Propag., 42 (1994), 993999.CrossRefGoogle Scholar
[5]Donelli, M.; Caorsi, S.; DeNatale, F.; Pastorino, M.; Massa, A.: Linear antenna synthesis with a hybrid genetic algorithm. Prog. Electromagn. Res., 49 (2004), 122.CrossRefGoogle Scholar
[6]Mahanti, G.K.; Pathak, N.; Mahanti, P.: Synthesis of thinned linear antenna arrays with fixed sidelobe level using real coded genetic algorithm. Prog. Electromagn. Res., 75 (2007), 319328.CrossRefGoogle Scholar
[7]Meijer, C.A.: Simulated annealing in the design of thinned arrays having Low sidelobe levels, in Proc. South African Symp. Communication and Signal Processing, 1998.Google Scholar
[8]Razavi, C.A.; Forooraghi, K.: Thinned arrays using pattern search algorithms. Prog. Electromagn. Res., 78 (2008), 6171.CrossRefGoogle Scholar
[9]Harrington, R.F.: Sidelobe reduction by nonuniform element spacing. IRE Trans. Antennas Propag., 9 (1961), 187192.CrossRefGoogle Scholar
[10]Lee, K.C.; Jhang, J.Y.: Application of particle swarm algorithm to the optimization of unequally spaced antenna arrays. J. Electromagn. Waves Appl., 20 (2006), 20012012.CrossRefGoogle Scholar
[11]Ayestar, R.G.; Las-Heras, F.; Martinez, J.A.: Nonuniform-antenna array synthesis using neural networks. J. Electromagn. Waves Appl., 21 (2007), 10011011.CrossRefGoogle Scholar
[12]Kazemi, S.; Hassani, H.R.: Performance improvement in amplitude synthesis of unequally spaced array using least mean square method. Prog. Electromagn. Res., 1 (2008), 135145.CrossRefGoogle Scholar
[13]Bray, M.G.; Werner, D.H.; Boeringer, D.W.; Machuga, D.W.: Optimization of thinned aperiodic linear phased arrays using genetic algorithms to reduce grating lobes during scanning. IEEE Trans. Antennas Propag., 50 (2002), 17321742.CrossRefGoogle Scholar
[14]Shi, Y.; Eberhart, R.C.: A modified particle swarm optimizer, in Proc. of the IEEE Congress on Evolutionary Computation, Piscataway, NJ, 1997.Google Scholar
[15]Jin, N.; Rahmat-Samii, Y.: Advances in particle swarm optimization for antenna designs: real-number, binary, single-objective and multiobjective implementations. IEEE Trans. Antennas Propag., 55 (2007), 556567.CrossRefGoogle Scholar
[16]Sacchi, C.; De Natale, F.; Donelli, M.; Lommi, A.; Massa, A.: Adaptive antenna array control in the presence of interfering signals with stochastic arrivals: assessment of a GA-based procedure. IEEE Trans. Wirel. Commun., 3 (2004), 10311036.CrossRefGoogle Scholar
[17]Donelli, M.; Azaro, R.; De Natale, F.G.B.; Massa, A.: An innovative computational approach based on a particle swarm strategy for adaptive phased-arrays control. IEEE Trans. Antennas Propag., 54 (2006), 888898.CrossRefGoogle Scholar
[18]Benedetti, M.; Azaro, R.; Franceschini, D.; Massa, A.: PSO-based real-time control of planar uniform circular arrays. Antennas Wirel. Propag. Lett., 5 (2006), 545548.CrossRefGoogle Scholar
[19]Storn, R.; Price, K.V.: Differential evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces, Tech. Report TR-95-012, Institute of Company Secretaries of India, Chennai, 1995.Google Scholar
[20]Storn, R.; Price, K.V.: Differential Evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim., 11 (1997), 341359.CrossRefGoogle Scholar
[21]Storn, R.; Price, K.V.; Lampinen, J.: Differential Evolution – A Practical Approach to Global Optimization, Springer-Verlag, Berlin, Germany, 2005.Google Scholar
[22]Das, S.; Abraham, A.; Chakraborty, U.K.; Konar, A.: Differential evolution using a neighborhood-based mutation operator. IEEE Trans. Evol. Comput., 13 (2009), 526553.CrossRefGoogle Scholar
[23]Elliott, R.S.: Antenna Theory and Design, IEEE/Wiley Interscience, New York, 2003.CrossRefGoogle Scholar
[24]Hansen, R.C.: Formulation of echelon dipole mutual impedance for computer. IEEE Trans. Antennas Propag., 20 (1972), 780781.CrossRefGoogle Scholar
[25]Schelknuff, S.A.; Friss, H.T.: Antennas Theory and Practice, Wiley, New York, 1952.Google Scholar
[26]Hollander, M.; Wolfe, D.A.: Nonparametric Statistical Methods, John Wiley & Sons, Hoboken, NJ, 1999.Google Scholar
[27]Moffet, A.T.: Minimum-redundancy linear arrays. IEEE Trans., 16 (1968), 172175.CrossRefGoogle Scholar
[28]Linebarger, D.A.; Sudborough, I.H.; Tollis, I.I.G.: Difference bases and sparse sensor arrays. IEEE Trans. Inf. Theory, 39 (1993), 716721.CrossRefGoogle Scholar
[29]Dollas, A.; Rankin, W.T.; Cracken, D.Mc.: A new algorithm for Golomb ruler derivation and proof of the 19 mark ruler. IEEE Trans. Inf. Theory, 44 (1998), 379382.CrossRefGoogle Scholar