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The Study of Sparse Arrays Aynthesis with Low Peak Side-lobe Level

Author: PanZuoZuo
Tutor: DengWeiBo
School: Harbin Institute of Technology
Course: Information and Communication Engineering
Keywords: sparse array genetic algorithm particle swarm optimization peak sidelobe grating lobe
CLC: TN957.2
Type: Master's thesis
Year: 2012
Downloads: 31
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Abstract


Sparse arrays have high cost performance which makes it popular in radarsystem. The bigger the aperture of antenna arrays, the thinner of beam and thestronger of arrays’ directivity.To the uniform linear arrays, the way of increasing thenumber of elements can meet the demand of the resolution ratio, but this will makethe system more complex and need more the cost of system’s construction. Fromanthor aspect, it also challenges the processing of the signals. If large aperature isneeded, the uniform linear array whose elements’distance is large, but grating lobemaybe appearance in this situation. So the uniform linear array doesn’t have enoughfreedom, it hasn’t enough parameters to adjust to satisfy the performance requst.To compare with the way which increments the number of antenna elements toincrease the aperture, using sparse arrays can decrement the structure cost of radarsystem. For example, the structure cost of solid-state active phased array antenna islarge, and it is the primer part of cost. On the side, in practical application, highresolution is required while the grain is not key factor in many systems. Theadvantage of sparse arrays is that they don’t have grating lobes, but they have highside-lobes. To lower the side-lobe level, this paper adopt intelligence algorithms tosynthesis the elements’ position of sparse arrays.In the first part of the content: the principle of the product of antenna arrays’directional diagram is the base of analysis the performance of the array, so theprinciple is demonstrated. Constructing the model of array is the premise to obtainthe formula, so the definition and model of sparse array and uniform linear array(ULA) are given. To have the direct-viewing understanding of the diagram and toshow the advantage of the sparse array, the directional diagrams of sparse array andULA are showed. The performance of the directional diagram is estimated by someparameters as followings: side-lobe level, main-lobe width, grain, directivity, bandwidth.In the second part of the content: the fundamental knowledge about three genetic algorithms is presented. Three essential operations and standard geneticalgorithm are introduced. Further the definition and operation of three modifiedgenetic algorithms can be given. So far, the genetic algorithm has an integrityunderstanding. Three modified genetic algorithms are used for the synthesis ofsparse array. The premises of simulation are involved with several kinds: the ULAexisting grating lobe or not; the number of array is odd or even; the elements aresymmetrical or not; the kinds of the optimized parameters.In the third part of the content: particle swarm optimization (PSO) is presented.The principle and flow diagram are given. To compare the fitness in the synthesis ofsparse array of the four algorithms, the outcomes of them are illustrated.In the fourth part of the content: to comprehend the degree of influence of theparameters of antenna arrays on the side lobe, the number of antenna arrays,aperture and the minimum distance constraint have been researched. Fitnessfunction controls the iteration process. It is transferred by the side lobe. To decreasethe side lobe level, the way of improving the fitness function is put forwarded. Theimprovement is to add the improved hamming window to the fitness function. Allabove studies are based on the premise that the radiation direction is normaldirection, so other situations are studied. Finally, the band width of sparse array isillustrated.

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