## 1 Introduction  
Positioning plays a crucial role in aircraft operations [1; 2]. Currently, aircraft navigation systems primarily consist of satellite navigation, satellite-aided inertia combined navigation, and visual navigation [3; 4]. Although satellite navigation systems can offer continuous and precise position information, they are susceptible to disruption and deception from anti-satellite devices, leading to incorrect positioning [5; 6]. Moreover, the navigation precision of satellite systems deteriorates rapidly when subjected to interference, resulting in errors. In contrast, the performance of an inertia and satellite combined navigation system heavily relies on the precision of the inertial navigation component [7; 8]. With high-precision inertial navigation equipment and stable satellite signals, this system facilitates stable aircraft positioning. Visual navigation, on the other hand, boasts low cumulative errors and strong anti-interference capabilities [9; 10]. Consequently, visual navigation has found extensive use in terminal guidance applications. To enhance the stability and safety of navigation systems while reducing equipment costs, visual navigation has emerged as the preferred choice [11]. Visual navigation can be broadly categorized into two main types: SLAM (Simultaneous Localization and Mapping), based on image sequences and scene matching, based on a single image [12; 13]. In visual SLAM, a visual speedometer can be established by obtaining the successive images' positioning posture transformation matrix and calculating the corresponding positioning posture information [14]. However, accumulative errors are inherent in this approach, and visual SLAM often requires a large number of distinctive feature points for successful matching, which can be challenging to achieve during high-altitude flights. On the other hand, scene matching offers a reliable alternative for aircraft navigation, as it does not suffer from accumulative errors and exhibits high stability. Scene matching involves determining the absolute positioning information of reference markers on the surface through image matching and subsequently solving for the positioning posture using dedicated algorithms. The precision of scene matching relies not only on the quality of image matching but also on the effectiveness of the algorithm employed. The PnP (Perspective-n-Point) algorithm is widely recognized as one of the most extensively used algorithms for solving positioning posture due to its excellent stability and efficiency [15; 16]. The calculation of PnP attitude relies on a known set of three-dimensional points, which are used to determine their corresponding two-dimensional projection points in the image. This allows for the estimation of the camera's altitude through the composition of a translation rotation matrix. The translation matrix describes the camera's position in the world coordinate system, while the rotation matrix characterizes its direction and orientation. Several common PnP algorithms are available, including Perspective-3-Point (P3P) [17], Efficient Perspective-n-Point (EPnP) [18], Direct Linear Transform (DLT) [19], and Uncalibrated Perspective-n-Point (UPnP) [20]. These algorithms are employed in varying situations. Among them, P3P is challenging to utilize in the SLAM algorithm due to difficulties in obtaining an exclusive solution and the requirement for multiple pairs of characteristic points for accurate calculations after optimization. The DLT and UPnP algorithms necessitate at least six pairs of high-quality matching points, making them less suitable for use in multi-modal image matching algorithms involving noisy images. However, EPnP only requires four correctly matched pairs of images to solve the positioning posture and can obtain an exclusive solution. Consequently, it is well-suited for aircraft-based positioning posture solving [21; 22].