A Modified Taguchi's Optimization Algorithm for Beamforming Applications

The present paper is a study of adaptive beamforming (ABF) techniques applied to antenna arrays. The structure of these techniques is based on Taguchi's Optimization (TagO) method. The high convergence speed and the ability to reach near-optimal solutions by adjusting only one parameter make the Taguchi's method an attractive choice for real time implementations like the case of ABF. Modiflcations are proposed in order to enhance the applicability of the TagO algorithm and decrease the computational time needed by the algorithm to terminate. The TagO method is used here to construct an ABF technique that aims at steering the main lobe of a uniform linear array towards a signal of interest, under the constraint of low side lobe level (SLL) or the constraint of placing radiation pattern nulls towards respective interference signals. Properly deflned fltness functions must be minimized by the TagO algorithm to satisfy respectively the above mentioned constraints. The TagO-based ABF technique is compared with typical beamforming methods, like the Sample Matrix Inversion (SMI) and Maximum Likelihood (ML) ones, and with two evolutionary ABF techniques based on Particle Swarm Optimization (PSO) and Difierential Evolution (DE), respectively. The comparison is performed regarding the convergence speed, the ability to achieve better fltness values in less time, the ability to properly steer the main lobe and flnally the null-steering ability or the SLL control depending on the constraint type. The results exhibit the superiority of the TagO-based technique.

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