# Research on Optimization of Target Positioning Error Based on Unmanned Aerial Vehicle Platform
## 1 Introduction  
Driven by the fourth technological age, the second machine revolution led by intelligent technology is reshaping the reconnaissance field. As a representative of innovation, air-based mobile platforms are at the core of reconnaissance by virtue of their innovativeness and practicability, showing increasing strategic value [1, 2]. The development of optoelectronic payloads enables UAVs to accomplish multiple tasks, such as target tracking and localization [3]. The continuous improvement of sensors, computer vision, and other fields has also led to significant developments in UAV target localization technology. Precise target localization is a key UAV technology, which can provide information support for subsequent mission planning [4, 5]. The existing localization methods can be divided into active and passive. Commonly, active localization involves deriving a target's position using laser ranging combined with chi-square coordinate transformation, which has high accuracy, but the laser action distance is limited, and this method does not meet the stealth and safety standards for UAVs [6]. Passive positioning does not transmit signals to the detected target; instead, it only uses received target radiation source signals for the reconnaissance and localization of the target. This approach has been widely studied by other scholars in recent years [7]. [PERSON] et al. [8] established a multi-target localization model, which reduced the target localization error by improving the filtering effect. [PERSON] et al. [9] studied the purely orientation-related passive localization problem of UAVs in an electromagnetic silent environment. [PERSON] et al. [10] established a single-objective optimization-based UAV passive localization model. Due to the complexity of these application scenarios, we must further reduce the localization error and improve localization accuracy. In the process of UAV target localization, multiple factors are intertwined and influence each other, thus triggering diverse error sources. Therefore, we must effectively handle and distribute these errors to improve the accuracy of UAV target positioning. Some methods that can effectively improve the target localization accuracy of UAVs are described below. [PERSON] et al. [11] established a single-station passive positioning equation, analyzed the target positioning error, discussed the error factors affecting positioning, and gave suggestions for application. [PERSON] et al. [12] viewed angle error allocation for a small micro-UAV-borne optoelectronic pod from the perspective of multi-objective optimization. The position or localization errors of UAVs can also be effectively eliminated using controllers. These controllers are designed to compensate for various factors that can cause inaccuracies in the UAV's position or localization. By utilizing algorithms and data processing, controllers can adjust the UAV's flight path and attitude in real time, ensuring that it maintains its intended position or trajectory with high precision. [PERSON] et al. [13] propose an enhanced version of the SEDA to address the problem of control performance limited by the fixed probability coefficient. [PERSON] et al. [14] established a multi-constraint penetration combat model that can realize the autonomous control of UAV attack trajectory in complex battlefield environments and multi-constraint conditions. [PERSON] et al. [15] developed a fuzzy controller system gain optimizer for UAVs based on a metaheuristic algorithm to optimize the mission tracking trajectory of UAVs. [PERSON] et al. [16] proposed an adaptive fuzzy neural fault-tolerant control scheme that integrates an inverse compensation strategy and a fuzzy neural network technique and demonstrated that the tracking error of a quadrotor UAV ultimately converges to a small, compact set by appropriately choosing the design parameters. However, traditional algorithms often have the disadvantages of high computational complexity, poor adaptability, strict initial condition requirements, etc.