Abstract
Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg's 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter.
Highlights
Atmospheric duct can change the electromagnetic wave propagation path and effective coverage areas, as it is formed in an anomalous atmospheric refractivity structure
In order to illustrate the performance of DACS-crossover operation (CO), the simulation and the real data experiment are both implemented, and the retrieval results are compared with the cuckoo search (CS), genetic algorithm (GA), and particle swarm optimization (PSO) algorithms
In order to verify the validation of DACS-CO algorithm with real radar clutter, the observed data obtained from the Wallops Island on April 2, 1998, experiment is selected
Summary
Atmospheric duct can change the electromagnetic wave propagation path and effective coverage areas, as it is formed in an anomalous atmospheric refractivity structure. It is very difficult to get the analytical solutions using RFC, because the relationship between refractivity parameters and radar clutter is clearly nonlinear and ill-posed To deal with this problem, several inversion algorithms have been used in RFC. Using the RFC technique to estimate atmospheric refractivity is a complex nonlinear and ill-posed optimization problem. In this paper, the new improved algorithm, DACS-CO, was selected as the optimization algorithm being utilized in the RFC technique to estimate the atmospheric refractivity. In order to illustrate the performance of DACS-CO, the simulation and the real data experiment are both implemented, and the retrieval results are compared with the CS, genetic algorithm (GA), and particle swarm optimization (PSO) algorithms.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
