Adaptive Detection and Correction of Faulty Elements in Frequency Diverse Array

Abstract

In array beamforming, main lobe steering towards intended position and null placement towards interferers’ positions are the main objectives. Unfortunately, if some array elements fail to work, the array beamforming performance is seriously deteriorated. Therefore, detection of faulty array element and correction of beampattern are two different issues but are very inter-linked tasks that need to be developed for efficient beamforming performance. In literature, these two tasks have been thoroughly investigated, separately. However, in this paper, we propose an adaptive closed-loop joint faulty element detection and beam pattern correction design. Moreover, we are considering frequency diverse array (FDA) with Bat algorithm (BA) based beamformer to detect the faulty elements first and, consequently, correct the beampattern to impose nulls in the interferences range-angle positions. The range-angle based pattern nulls are obtained by controlling the weights of the remaining array elements. The convergence performance of the FDA with Bat algorithm design has been compared with that of genetic algorithm (GA) and particle swarm optimization (PSO), while SINR performance of healthy and faulty arrays is compared for an interference dominant case.