Robust Array Beamforming via an Improved Chicken Swarm Optimization Approach

Abstract

Robust array beamforming is a challenging task in radar, sonar and communications due to the influence of direction of arrival (DOA) mismatch and sensor position errors. However, how to enhance the robustness of beamforming is a key issue in antenna arrays. The current paper focuses on a novel approach called the improved chicken swarm optimization (ICSO) method to settle the optimization model of conventional linearly constrained minimum variance (LCMV) based on support vector machine (SVM) to against the mismatch problems as well as control the sidelobe level (SLL). As far as the ICSO method is concerned, considering that the particle swarm optimization (PSO) algorithm has outstanding convergence performance in the early iteration, the dominance of the alpha wolf in the grey wolf optimization (GWO) algorithm and the innovative mutual attraction mechanism in the firefly algorithm (FA), and we introduce these three strategies into the solution update method of conventional chicken swarm optimization (CSO) algorithm for achieving better optimization capability. Moreover, an operation of removing duplicate solutions is proposed to enhance the utilization of the population. In terms of the SVM-based LCMV beamforming algorithm, we adopt the so-called linear ε -insensitive loss function to reconstruct the final cost function of LCMV by penalizing the errors between the actual and ideal array responses. Finally, we conduct simulations to evaluate the performance of the swarm intelligent optimization algorithms under an ideal scenario without mismatch and an actual scenario with the mismatch, respectively. And the results demonstrate that the developed ICSO algorithm obtains excellent robustness for different scenarios compared to PSO, FA, GWO and CSO optimization algorithms.INDEX TERMS Robust array beamforming, improved chicken swarm optimization, linearly constrained minimum variance, support vector machine, sidelobe level, steering vector errors.

Green Communication in Internet of Things: A Hybrid Bio-Inspired Intelligent Approach

“…As mentioned above, recent studies [ 13 , 14 , 15 , 16 , 17 , 21 , 22 , 23 , 24 ] through simulation have observed that artificial intelligence and precisely bio-inspired techniques are preferable to traditional probabilistic and deterministic approaches subject to optimizing the energy consumption in IoT networks. In [ 17 , 21 ], the authors proved the proposed algorithm based on CSO obtains excellent performance over traditional approaches such as PSO, FL and GA for robust beam-forming approach, whereas authors in papers [ 24 , 25 ] CSO-based clustering routing protocol plays a significant role in reducing energy consumption over integration with bio-inspired approaches.…”

“…Recent studies have shown that meta-heuristics approaches are more suitable for approximately solving NP problems for CH selection [ 20 ]. Consequently, proper optimization methods such as fuzzy logic inference [ 13 , 14 ], bat algorithm [ 15 ], particle swarm optimization [ 16 , 17 , 18 ], differential evolutionary and harmony search [ 19 , 20 ], genetic algorithm (GA) [ 21 ], and bio-inspired chicken swarm optimization (CSO) have the potential to be effectively used for finding the optimal number of CHs [ 22 , 23 , 24 ].…”