Null Steering of Adaptive Beamforming Using Linear Constraint Minimum Variance Assisted by Particle Swarm Optimization, Dynamic Mutated Artificial Immune System, and Gravitational Search Algorithm

Abstract

Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program.

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

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Subarray‐based time‐delay low sidelobes methods for space‐time coding array

“…As a result, measures should be taken to lower the SLL. Previous studies showed that artificial intelligence optimisation algorithms such as genetic algorithms [17], artificial bee colony [18], and particle swarm optimisation [19, 20] were successfully applied to handle a variety of problems in order to improve various issues in the antenna system [21, 22]. However, these methods increase the system complexity.…”

Multiple Target Localization with Bistatic Radar Using Heuristic Computational Intelligence Techniques

“…Thus, to localize the near field targets, the range information must also be incorporated into the signal model along with DOA [3,4]. Near field targets localization, especially 3D (range, elevation angle, and azimuth angle), plays significant role in radar, cognitive radio networks, and array signal processing, since it is a preliminary step for adaptive beamformer to guide the main beam in preferred direction and simultaneously manage the nulls in the direction of jammers [5][6][7].…”