Performance Evaluation of Beamforming Array Antennas Based on Partial Update LMS and NLMS Adaptive Algorithms

The well-known phased array antenna (PAA) is the most common and efficient way for providing adaptive Beamforming Array Antenna (BAA), which is an array of antenna elements (N) controlled by signals with well-defined phase relations between those elements. There are many proposals aimed at reducing the need for phase shifters, however these initiatives will fail until additional active control units are introduced. Despite the fact that these problems have been researched by numerous Uniform Linear Array (ULA) researchers, Partial Update (PU) adaptive filtering has never been implemented. When increasing or decreasing the number of antennas (M) for each ULA element, such that M < N, the model suggested in this study selects or deselects the RF chain utilizing PU LMS or NPULMS update partial adaptive algorithms. The ULA simulation results show that the PU LMS and NPULMS algorithms are as effective as the full band LMS and NLMS algorithms in terms of mean square error (MSE), tracking, and estimate input signal performance, but with a faster convergence time and lower steady-state error signal level. The main beam shape of the PU algorithms is very near to that of the planned uniform array pattern, and they can also reduce interference signals by null steering. However, NPULMS outperforms the PULMS algorithm in terms of efficiency.