A Novel Probability Density Taper Approach for Planar Array Thinning

Abstract

In this paper, a new approach for the synthesis of thinned periodic planar arrays featuring a minimum sidelobe level is presented. The method is based on the probability learning iterative Fourier technique (PLIFT) with a fitness function to determine element distributions for large planar array thinning, which is denoted as FPLIFT here. Compared with the traditional methods, the PLIFT acquires a minimum sidelobe level and avoids the problem of local optimum. The fitness function is used to initialize the starting parameters of the position of the elements. Then, a probability density taper is adopted to model the element distributions across the aperture. The efficiency of the FPLIFT method was validated by a representative example of array thinning with minimum peak sidelobe level. It can be demonstrated by the simulated results that the FPLIFT inherits PLIFT advantages, and results in high efficiency in reducing the side lobes. Therefore, this proposed FPLIFT can be applied in synthesis and optimization of large-scale antenna arrays.