Estimation of the Atmospheric Duct From Radar Sea Clutter Using Artificial Bee Colony Optimization Algorithm

Abstract

Abstract-In this study, the Artificial Bee Colony Optimization (ABCO) algorithm has been proposed to estimate the atmospheric duct in maritime environment. The radar sea clutter power is calculated by the parabolic equation method. In order to validate the accuracy and robustness of ABCO algorithm, the experimental and simulation study are respectively carried out in the current research. In the simulation study, the statistical analysis of the estimation results in term of the mean squared error (MSE), mean absolute deviation (MAD) and mean relative error (MRE) are presented to analyze the optimization performance with different noise standard deviation, and the comparative study of the performance of ABCO and particle swarm optimization (PSO) algorithm are also shown. The investigation presented indicate that the ABCO algorithm can be accurately and effectively utilized to estimate the evaporation duct and surface-based duct using refractivity from clutter (RFC) technique in maritime environment. In addition, the performance of ABCO algorithm is clearly superior to that of the PSO algorithm according to the statistical analysis results, especially for the four-parameter surface-based duct estimation.

Atmospheric refractivity estimation from AIS signal power using the quantum-behaved particle swarm optimization algorithm

“…Therefore, electromagnetic waves propagating in the atmosphere are affected by atmospheric refraction. Atmospheric refractivity is defined by [15] N = (n − 1) × 10 6 (1)…”

Inferring the atmospheric duct from radar sea clutter using the improved artificial bee colony algorithm

“…Zhang et al introduced a four-parameter modified refractivity profile model for the evaporation duct estimation with RFC [11]. The artificial bee colony (ABC) algorithm [12, 13] inspired by the intelligent foraging behavior of honey bee swarm is one of the most recently proposed bio-inspired swarm intelligence algorithm, and it is applied to the electromagnetic optimization problem [13] and atmospheric duct estimation [14, 15]. Yang employed the ABC to the atmospheric duct estimation, and the results showed that the performance of ABC is better than particle swarm optimizer according to the comparative analysis results [14].…”

“…The artificial bee colony (ABC) algorithm [12, 13] inspired by the intelligent foraging behavior of honey bee swarm is one of the most recently proposed bio-inspired swarm intelligence algorithm, and it is applied to the electromagnetic optimization problem [13] and atmospheric duct estimation [14, 15]. Yang employed the ABC to the atmospheric duct estimation, and the results showed that the performance of ABC is better than particle swarm optimizer according to the comparative analysis results [14]. Yang et al proposed the ABC algorithm combined with opposition-based learning and global best search equation (OGABC) to improve the problems of the slow convergence speed and sinking into local optima appear in the duct estimation problem, and investigation results indicate that the OGABC achieves good performance compared with the ABC and the modified invasive weed optimization [15].…”

Investigation on the Inversion of the Atmospheric Duct Using the Artificial Bee Colony Algorithm Based on Opposition-Based Learning

“…Taking the nonlinear relation between the forward propagation model and atmospheric duct parameters into consideration, the investigation on the optimization models with high performance is one of the most important research topics in the field of inversion of the atmospheric duct from radar sea clutter. For instance, the least square support vector machine, the particle swarm optimization (PSO), the simulated annealing algorithm, and the ABC algorithm have been applied to infer the atmospheric duct using RFC technique [8][9][10][11].…”

“…Currently, the ABC algorithm has been applied to the design of antenna and electromagnetic devices [13][14][15]. In addition, the ABC algorithm has been used to infer the atmospheric duct from RFC technique [11], and the comparative study results demonstrate that the performance of ABC is superior to that of the PSO for the inversion of atmospheric duct. However, the ABC also has the drawbacks of easily falling into local optima and slower convergence rate.…”