# Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points
## 1 Introduction  
With the development of related technologies such as optical design, image processing, and autonomous control, as one of the essential equipment generally loaded on aerial remote controllers, aerial cameras are developing towards the direction of having both high-resolution imaging and high-precision positioning capabilities. In order to realize real-time aerial photogrammetry, the aerial camera needs to independently locate the target while acquiring target images [1; 2; 3; 4]. Aiming at improving positioning accuracy, the aerial triangulation method is widely applied in traditional aerial mapping practice. This method needs to set control points with known positions of the target area and calculate according to the information of the target points and control points to eliminate errors to gain more accurate results. In the circumstance that ground control points lack, aerial cameras are expected to autonomously locate the target [5; 6; 7; 8; 9; 10; 11]. On the basis of whether the aerial camera is equipped with an active signal transmitting device, target positioning algorithms can be divided into two categories. The algorithm that needs to be positioned by transmitting signals is called sourced positioning algorithm. Contrarily, the defined Passive localization algorithm does not need to actively transmit signals and only relies on its parameters for positioning. In light of many former research results, the earth ellipsoid model is widely accepted as a mainstream method in the location process [12; 13; 14; 15; 16; 17] for the purpose of realizing the target location with a single image. For instance, the target localization algorithm based on the earth ellipsoid model proposed by [PERSON], [PERSON] [12] realizes the localization of the ground target by the aerial camera through a single image while reducing the influence of the curvature of the earth on the localization outcome. In addition, the sourced positioning algorithms proposedgradually achieve higher level accuracy through using distance information generated by devices such as the laser range finder (LRF) or synthetic aperture radar (SAR) [18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. By utilizing the angle information provided by the airborne photoelectric platform, [PERSON] [26] established a multi-target positioning model and proposed the pixel line-of-sight vector method through the parameters of the laser range finder and angle sensor. The positioning results obtained by a single measurement are greatly affected by random errors. Two mainstream methods for solving this issue include multi-machine intersection positioning and a single aerial camera that accomplish multiple measurements [28; 29; 30; 31; 32; 33; 34; 35]. Another related theory proposed to reduce the influence of random errors is filtering algorithms [36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49]. Such kinds of algorithms are widely used in the field of target tracking. No matter what the positioning algorithm is, it will be affected by error factors. The angle and measurement accuracy, target height, image distortion, atmospheric environment, and other potential error sources will lead to the reduction in target positioning accuracy. Therefore, a large amount of analysis focusing on the error factors of the positioning algorithm has been conducted in recent experiments [50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62]. [PERSON] [50] analyzed the imaging link of the aerial camera for photogrammetry of the target and established a multifactor positioning error analysis model. Furthermore, scholars have established corresponding mathematical models or correction algorithms for different error factors [63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73].