# Study of an Atmospheric Refractivity Estimation from a Clutter Using Genetic Algorithm
## 1 Introduction  
Advancements in radar technologies have led to significant improvements in the performance of long-range shipborne radar systems, which have been extensively adopted for searching and tracking enemy targets [1; 2; 3; 4; 5]. However, in such radar systems, the radar performance is sometimes degraded by weather conditions [6; 7; 8; 9], because the propagation path of the radar signal varies when the atmospheric refractivity changes. Therefore, long-range shipborne radar systems require a real-time observation of the atmospheric conditions. To observe atmospheric conditions, a rawinsonde that can measure the atmospheric conditions at high altitude is typically used [10; 11; 12]. However, it is difficult to obtain the atmospheric conditions in real-time since the rawinsonde is generally operated for a limited time of less than three times a day. To achieve real-time observations of atmospheric conditions, several studies focusing on estimating refractivity from clutter (RFC) have been conducted. The RFC techniques utilizing radar sea-clutter images are often combined with various estimation methods, such as the Kalman filter [13], Monte Carlo simulation [14], and other optimization algorithms [15; 16; 17; 18]. However, most of these studies have been conducted within the limited space around Wallops Island [13; 14; 15; 16; 17; 18; 19]. In addition, since the radar sea-clutter images are significantly affected by the sea wave height, it is difficult to obtain constant images, even if the atmospheric environment is assumed to be the same [20; 21; 22]. More recently, a study on sea-surface modeling using a deep learning method that recognizes the real-time changing state of sea waves has been reported [23].  
In this paper, a method for estimating the atmospheric refractivity using both the sea and land clutters is proposed. The atmospheric refractivity can be estimated usingthe clutter powers, because the clutter powers are varied in real-time depending on the atmospheric refractivity. Since land clutter powers do not change much if the atmospheric environment is constant [24], the clutter powers should be obtained not only for the sea but also for the land. In the actual radar operation, more consistent prediction results can be obtained by analyzing clutter, including land terrain. To estimate the atmospheric refractivity using the clutter powers, the real terrain model around a coastal area, including a number of islands and inland in Korea, is applied to Advanced Refractive Prediction System (AREPS) electromagnetic (EM) propagation simulation software [25]. Then, different RCS models are applied to land and sea. In addition, an artificial tri-linear model that can express all atmospheric conditions, including normal, super, sub and duct, is designed. Based on the artificial tri-linear model, the clutter power spectrums are calculated using the AREPS EM simulator. At the same time, the clutter power spectrums are again obtained based on the measured atmospheric refractivity data using the AREPS simulator. Herein, data from four dates, including the duct condition among the 2021 data measured by the Heuksando Meteorological Observatory, are utilized. In actual operation, this spectrum of measured reflectivity can be replaced with real-time clutter spectrums collected from radars. A cost function for the genetic algorithm (GA) is then defined based on the difference between the use of the two calculated clutter power spectrums to predict the atmospheric refractivity. The optimum variables of the tri-linear model are determined at a minimum cost in the GA process. The results demonstrate that atmospheric refractivity can be predicted from the clutter powers using the proposed method.