# Systematic Error Correction for Geo-Location of Airborne Optoelectronic Platforms
## 1 Introduction  
Airborne optoelectronic platforms, which can realize a wide range of search, identification, tracking and measurement tasks, are playing an increasingly important role in military and civilian applications such as search, rescue of the wounded and target reconnaissance, etc. [1, 2, 3, 4]. The process of obtaining geographic coordinate information of a target using an airborne optoelectronic platform is called geo-location. Accurate geo-location is of great significance to the rescue of the wounded, target reconnaissance, etc. In recent years scholars have conducted extensive research on geo-location algorithms. The following methods are used to improve the geo-location accuracy:  
1. Build a more accurate model based on the Earth ellipsoid model or digital elevation model. For example, [PERSON] proposed a geo-location algorithm based on the Earth ellipsoid model from a single image using an aerial camera to reduce the influence of the Earth's curvature on the positioning result [5]. [PERSON] proposed a geo-location algorithm based on the digital elevation model (DEM) for an airborne wide-area reconnaissance system, and the simulation results show that the proposed algorithm can improve the geo-location accuracy of ground target in rough terrain area [6]. The Global Hawk unmanned aerial vehicle (UAV) geo-location system is also based on an Earth ellipsoid model to calculate the geodetic coordinates of the image center [7]. 2. Image the target multiple times or using multiple sensors or multiple UAVs to get redundant information to improve geo-location accuracy. [PERSON] proposed an improved two-UAV intersection localization system based on airborne optoelectronic platformsusing the crossed-angle localization method to address the limitation of the existing UAV photoelectric localization method used for moving objects [8]. [PERSON] proposed an information-sharing strategy by allocating sensors to multiple small UAVs to solve the drawbacks that a small unmanned aerial vehicle (UAV) cannot be equipped with many sensors for target localization [9]. [PERSON] and [PERSON] described an active target-tracking strategy to deploy a team of UAVs along the paths that minimize the uncertainty about the position of a moving target [10]. [PERSON] proposed a ground target cooperative geometric localization method based on the locations of several unmanned aerial vehicles (UAVs) and their relative distances from a target, and simulation results from the MATLAB/Simulink toolbox show that this method is more effective than a traditional approach [11]. 3. Use filtering algorithms or video sequences to optimize positioning results. [PERSON] et al. [12] proposed an adaptive tracking algorithm based on the Kalman filter (KF). [PERSON] proposed a moving average filtering (MAF) to improve the geo-location accuracy of moving ground target, which adapts to both constant velocity motion model and the turn model [6]. [PERSON] proposed a recursive least squares (RLS) filtering method based on UAV dead reckoning to improve the accuracy of the multi-target localization [13]. Nonlinear filter [14; 15; 16; 17] and methods based on video sequence [18; 19; 20; 21; 22; 23] are also proposed to estimate the locations of targets. 4. Analyze the factors causing the error and use calibration methods to make corrections. [PERSON] proposed a system and method for correction of relative angular displacements between an unmanned aerial vehicle (UAV) and its onboard strap-down photoelectric platform to improve localization accuracy [24]. With the Lens distortion parameter obtained in the laboratory, [PERSON] proposed a real-time zoom lens distortion correction method to improve the accuracy of the multi-target localization [13]. In all the above references, the authors either didn't take into account the effect of the systematic error [4; 19; 24; 25], or just introduced it as a fixed bias [6; 8].