## 1 Introduction
Image matching of aircraft always uses monocular vision [23; 24], which may lead to difficulty in acquiring profound information; consequently, during normal processing, pictures collected are always recognized as planes and the absolute coordinates acquired are always the points within the plane. Additionally, errors may have occurred if we took real-time pictures as a plane to solve the positioning posture. \(\Delta\)H is the error, shown in the following Figure 1. Because it involves a large number of formulas and coordinate system transformations, the right-handed coordinate system is used in the full text of this article. The whole text \(\theta\) represents the angle with the Z-axis, \(\varphi\) represents the angle with the X-axis, R represents the radius of the Earth, \(\Delta\)H represents the error, and H is the height of the camera from the ground. In the formula, '\(\times\)' represents matrix multiplication, whereas '\(\cdot\)' represents number multiplication. As is shown in Figure 1, O\({}_{1}\) denotes the position of the camera, O\({}_{2}\) denotes the center of the Earth, and the surface formed by NM represents the surface of the map that the  
Figure 1: PNP surface error model. camera can capture. The camera position O\({}_{1}\) is shown to be placed directly above the positive Z-axis for reader comprehension. However, in the actual computational process, the camera position O\({}_{1}\) is situated at a general location. The dotted line in the figure is used to make the Earth look more three-dimensional. In the imaging process, since the sphere is approximated as a class plane when the height of the camera increases, an error will generate, which will reduce the accuracy of the positioning attitude solution. Therefore, it is necessary to determine the threshold value of the central angle to estimate the ideal errors of a plane in order to replace the sphere surface and the transmission law. To address this problem, we put forward an approach for a spherical EPnP positioning posture solution by measuring the central angle threshold value for scene matching of aircraft. Based on the above approach, we constructed theoretical models for the flying height and camera field angles, which we named the Earth Model Efficient Perspective-n-Point method (EarM_EPnP). The innovations of the EarM_EPnP method are mainly as follows:  
1. In this paper, an aircraft positioning posture-solving approach is proposed, utilizing analogous three-dimensional data as input. This method significantly enhances the precision of solving flat coordinates through the application of the EPnP algorithm. 2. The replacement of three-dimensional solving in scene matching with a plane ensures the determination of the critical value of the central angle. This theoretical advancement lays the foundation for future investigations into the positioning posture solving of scene matching. 3. The parameters of the camera, critical angle, and the inherent relationship among field angles were effectively determined in this study. These findings establish the corresponding functional relationships and provide essential measurement principles for subsequent research in this field. This paper consists of four main sections. The introduction, the first part, provides an overview of the current status of research into solving positioning posture through scene matching. The second part focuses on the unique methodologies employed in this study. Subsequently, the third part presents the experimental findings and corresponding discussions. Finally, the conclusion summarizes the key findings and implications.