Offshore Surface Evaporation Duct Joint Inversion Algorithm Using Measured Dual-Frequency Sea Clutter

Abstract

In this paper, high-precision joint inversion of evaporative duct based on dual frequency radar sea clutter data is analyzed to study the abnormal duct environment phenomenon that occurs over offshore surfaces. As the information of duct environment retrieved by radars with different frequencies is inconsistent, a joint optimization model with dynamic penalty factor is proposed, which can improve the degree of conformity between the measured clutter power and the modeled clutter power. Then, a parallel crossover quantum particle swarm optimization algorithm is used to jointly invert the objective function, which adaptively processes the inputs involved in the crossover, and effectively improves the convergence of the inversion. Compared with the single-frequency model commonly used in engineering, the average relative error of the duct height of the dual frequency joint optimization model is reduced by 3.13%, and the average relative error of the duct intensity is reduced by 6.34%, verifying the effectiveness of this method. *

Research on Optimization Method of Evaporation Duct Prediction Model

“…It was initially conceived to visually emulate the graceful and unpredictable motions of birds. This algorithm boasts a considerable probability of approximating the global optimal solution, alongside its swift computational speed and superior global search capability compared to conventional optimization algorithms [36]. Assuming that the parameters to be optimized are D-dimensional vectors, in the search space, F particles are randomly generated and the position of each particle represents a set of parameters to be optimized, then the position of the f th particle is denoted as…”