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 $\varepsilon $ - 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.
Highlights
Robust adaptive beamforming has received considerable attention in the past years due to its necessity for radar, sonar, astronomy, wireless communications, medical imaging, audio signal processing and many other areas [1]–[3]
According to the theoretical characteristics of support vector machine (SVM), it is necessary to reconstruct the optimization model of the linearly constrained minimum variance (LCMV) beamforming: min w yp − wHa θp ε p=1 where yp − wHa θp ε = max 0, yp − wHa θp − ε is known as ε - insensitive loss function, which allows errors of array response for the assumed signal arrival angle θs smaller than ε, and the parameter ε is a non-negative real number that is used to define the set of admissible solutions
PROPOSED improved chicken swarm optimization (ICSO) APPROACH we focus on the proposed ICSO method, which is improved for conventional chicken swarm optimization (CSO) to settle robust beamforming based on SVM
Summary
Robust adaptive beamforming has received considerable attention in the past years due to its necessity for radar, sonar, astronomy, wireless communications, medical imaging, audio signal processing and many other areas [1]–[3]. Saxena et al [33] use the GWO algorithm to obtain the optimized antenna positions and current amplitudes in order to achieve the best pattern synthesis This method provides a considerable enhancement to the optimization of the linear antenna array. Li et al [34] adopt improved biogeography-based optimization (BBO) to optimize the linear and circular antenna array beam patterns It does not provide the performance of the algorithm for the high-dimensional optimization problem. The conventional CSO provides a commendable idea, each solution update method is not effective, which results in a decrease in the overall search ability of the algorithm [39], [40] These circumstances above prompt us to propose an improved version of the conventional CSO algorithm for solving the SVM robust beamforming optimization model. By means of improved mechanism, ICSO can avoid premature convergence and jump out of the local optimal solution to the global. 3) We conduct multi-dimensional simulations to further confirm the robustness of the proposed ICSO algorithm for steering vector mismatch
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