Beamforming is one of the most significant processes in smart antennas. To change the antenna beam pattern is the major function of beamforming for a given angle. The algorithm that is adaptive beamforming is utilized to choose the conventional weights of each array element from acquired data of array antenna to extract the desired source signal while cancelling noise and interference. A lot of algorithms are already existing for antenna beamforming technique but they all experience low convergence. So this paper deals with an iRLS algorithm for antenna beamforming technique with Particle Swarm Optimized (PSO) FFT filter. As a result, antenna beamforming based on iRLS shows fast convergence with reduced design complexity. The overall work is simulated in MATLAB. The parameters like amplitude, bit error rate, capacity, SINR and error function values are evaluated. Our work is compared with existing Applebaum, Recursive Least Squares (RLS) (with/without filter) and Least Mean Square (LMS) algorithm. Fast convergence is occurred by iRLS when compared with existing algorithms. The iRLS is converged at 90^th iteration, whereas existing algorithms likewise RLS with pre-filter, RLS without pre-filter, LMS and Applebaum is converged at 200, 400, 600 and 850^th iteration. So here, our proposed iRLS gives better performance when compared with others.

波束成形是智能天线中最重要的过程之一。对于给定的角度，改变天线波束方向图是波束成形的主要功能。利用自适应波束成形算法从阵列天线的采集数据中选择每个阵列元件的常规权重，以提取所需的源信号，同时消除噪声和干扰。天线波束成形技术已经存在很多算法，但是它们都收敛度低。因此，本文针对带有粒子群优化（PSO）FFT滤波器的天线波束形成技术的iRLS算法进行了研究。结果，基于iRLS的天线波束成形显示出快速收敛且降低了设计复杂性。总体工作在MATLAB中进行了仿真。诸如幅度，误码率，容量，评估SINR和误差函数值。我们的工作与现有的Applebaum，递归最小二乘（RLS）（带/不带滤波器）和最小均方（LMS）算法进行了比较。与现有算法相比，iRLS实现了快速收敛。iRLS收敛于90^第三次迭代，而现有算法同样具有预过滤器的RLS，不具有预过滤器的RLS，LMS和Applebaum都在^第200、400、600和850^次迭代中收敛。因此，在这里，我们提出的iRLS与其他系统相比具有更好的性能。