ANN-Assisted Beampattern Optimization of Semi-Coprime Array for Beam-Steering Applications

Abstract

In this paper, an artificial neural network (ANN) has been proposed to estimate the required values of the adjustable parameters of a Semi-Coprime array with staggered steering (SCASS), which was proposed recently. By adjusting the amount of staggering and the sidelobe attenuation (SLA) factor of Chebyshev weights, SCASS can promise a quite small half-power beamwidth (HPBW) and a high peak-to-side-lobe ratio (PLSR), even when the beam is steered away from broadside direction. However, HPBW and PSLR cannot be improved simultaneously. There is always a trade-off between the two performance metrics. Therefore, in this paper, a mechanism has been introduced to minimize HPBW for a desired PSLR. The proposed ANN takes the array of architectural parameters, the required steering angle, and the desired performance metric, i.e., PSLR, as input and suggests the values of the adjustable parameters, which can promise the minimum HPBW for the desired PSLR and steering angle. To train the ANN, we have developed a dataset in Matlab by calculating HPBW and PSLR from the beampattern generated for a large number of combinations of all the variable parameters. It has been shown in this work that the trained ANN can suggest the optimum values of the adjustable parameters that promise the minimum HPBW for the given steering angle, PSLR, and array architectural parameters. The trained ANN can suggest the required adjustable parameters for the desired performance with mean absolute error within just 0.83%.