# Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points
## 4 Discussion  
This section will briefly summarize the future development direction of localization algorithms; the positioning accuracy can be further improved in the following four directions:  
(1) _More accurate target positioning model_. The positioning algorithm calculates the geo-location of the target through the imaging angle and imaging distance, which directly affect the positioning accuracy, so improving the accuracy of various sensors is a feasible way to reduce positioning error. In addition, whether the mathematical model of the algorithm is perfect will also affect the positioning accuracy. Due to the complexity of the aerial camera structure, adding auxiliary coordinate systems such as the base coordinate system or the mirror coordinate system, and establishing a more refined mathematical model are significant for improving the positioning algorithm of different camera structures. Figure 15: Schematic diagram of positioning error with building height. \begin{table}
\begin{tabular}{c c} \hline \hline
**Error Classification** & **Error Influence** \\ \hline Atmospheric scattering & Visual axis pointing deviation \\ Atmospheric turbulence & Visual axis stability deviation \\ Image distortion & Target projection deviation \\ Height of target & Real position deviation \\ Visual axis control accuracy & Reprojection pointing deviation \\ Target detection accuracy & Pixel deviation of target to be located \\ Accuracy of imaging register & Homonymous point pixel deviation \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Analysis of often ignored errors. This paper verifies this view through a simulation model. On the basis of the traditional basic model, the camera coordinate system origin correction matrix, the damper auxiliary coordinate system, and the quick reflector coordinate system are gradually added to analyze the change in the positioning error. The positioning error of the basic model is about 67.8 m, and after adding a variety of auxiliary coordinate systems, it will approximate 58.2 m. Therefore, the results illustrate that the positioning accuracy of the algorithm is progressively improved, as shown in Figure 16.  
Some studies [17; 22; 25; 39] add auxiliary coordinate systems such as the base coordinate system and the reflection coordinate system according to the actual situation to optimize the unique structures of different aviation photoelectric loads. It has improved the positioning accuracy by about 12-23% compared with relying only on the basic coordinate system, which proves that a more accurate positioning model can play a very good optimization effect in the case of fixed cameras. (2) _Random error correction matrix or equation_. At present, the optimization research of positioning algorithms focuses on trying to reduce the influence of random errors. Various filtering algorithms are aimed at the problem of excessive random errors in positioning algorithms. This paper also analyzes the filtering method by using the simulation environment. For the application scenario of uniform moving target location, the basic location model and EKF-based target location algorithm are used to calculate the target position. As shown in Figure 17, due to the random error, the basic model must obtain the target position with an unstable error, and it is difficult to build a stable target motion state and route. Incorporating the EKF algorithm into the target location model can effectively reduce the impact of random errors, and the calculated route is basically consistent with the actual route of the target. The existing research [42; 43; 44; 45; 46] shows that the algorithm based on KF, EKF, or UKF can improve the target positioning accuracy of UAV to less than 20 m, which proves the effectiveness of the filtering positioning algorithm under the premise of a large number of continuous observations. The algorithm of target re-projection or Google Earth reference image positioning proves the effectiveness of the error correction matrix.