## 1 Introduction  
With the continuous development of national defense science and technology, aircraft play a pivotal role in aerospace, navigation, electronic communication, and other military industries. The landing point hit rate of an aircraft is a very important tactical indicator, and landing point measurement is of great significance to aircraft landing point evaluation [1, 2]. In recent years, many scholars have carried out research on flight landing forecasting. [PERSON] et al. [3] estimated the flight path and landing point coordinates of a damaged aircraft using numerical calculation methods; [PERSON] et al. [4] proposed a method to accurately predict the flight altitude of unmanned aerial vehicles using 3D point cloud sensors in a cluttered and dynamic indoor environment; [PERSON] et al. [5], based on the embedable ConvNet theory, proposed a method that can accurately predict the landing area of the aircraft and make the predicted landing area as small as possible; [PERSON] et al. [6], based on the vision system design theory, proposed a method to enable an unmanned aerial vehicle to automatically land on the designated ship flight deck position, improving the accuracy of aircraft target tracking; [PERSON] et al. [7] designed a missile landing point prediction method that can be as close as possible to the target and can be specified from a specific direction by exploring the target of the precision guidance problem. [PERSON] and [PERSON] et al. [8, 9] proposed an index prediction method for aircraft landing process based on DASH-link data. Based on machine learning and Bayesian neural networks, the landing vertical velocity of aircraft during landing is predicted numerically, and the landing distance of aircraft is predicted based on the multi-step rolling prediction strategy. The prediction model is verified by the test data of several flights. In particular, [PERSON] et al. [10] collected a lot of data on aircraft deviation accidents on the runway from several countries with similar aviation accident rates, and then predicted the landing position of aircraft on the existing runway so as to remove obstacles near the runway when necessary to ensure safety. However, the above studies all require a large number of actual aircraft landing points to provide data support and have high requirements for the sample capacity and confidence of the aircraft's field data. If a large number of field tests are carried out on an aircraft to study its landing point, there are many problems, such as high equipment cost, a large amount of deployment engineering, difficulty in organization and implementation, and potential safety hazards, so usually only a small amount of field landing point data information can be obtained [11]. To satisfy the demand for the sample capacity of aircraft landing points, many domestic and foreign research institutions have established semi-physical simulation laboratories [12]. Many researchers use computers to simulate target shooting and design and establish semi-physical simulation system models, which greatly reduces the test cost and provides test data, improving the sample capacity and confidence of hit assessment. As a typical method that can replace the real environment and test equipment, semi-physical simulation technology connects some entities of a research system to a simulation test loop and has become an important means and construction tool for the research, design, and development of aircraft equipment systems. More placement information has been obtained through simulation design [13; 14]. Some scholars have established a landing point prediction model based on a multisource data fusion method for aircraft landing point prediction. Through the effective fusion of field test data and the semi-physical simulation data of the coordinates of the landing point of an aircraft, the computer automatically processes a number of predicted landing points and then combines the artificial experience to predict the aircraft landing point.