Dimensionality Reduction And Beamforming Optimization For Large Scale Phased Array

Large and ultra large scale phased arrays have been more and more widely appliedbecause of its high gain and directiveness in the desired directions. However, theenormous feed networks and receiving/transmitter channels, required by digitalbeam-forming for the whole array, need huge cost and have poor real time. A novelalgorithm optimized for both subarray division and synthesis subarray weighting of sumand difference patterns is proposed in the paper. Simulate and digital multiple sum anddifference beams of large scale array are formed through the algorithm. The array isdivided into some subarrays through simulated annealing algorithm, genetic algorithm,particle swarm optimization and particle swarm optimization algorithm based on chaossearch and the sum and difference weights of elements and subarrays are jointlyoptimized to approach the ideal pattern, in order to avoid grating lobes and acquirelower sidelobe, deep null of difference patterns and fewer cost of beam gain. Theparticle swarm optimization algorithm is adopted to select the adaptive elements of thepartly adaptive array. The simulation results and performance analysis are given todemonstrate the effectiveness and feasibility of the presented algorithm.