# Clustering Method of Large-Scale Battlefield Airspace Based on Multi A * in Airspace Grid System
## 6 Conclusions  
In this paper, with the help of technical tools such as airspace reference grid generation system, image processing and path planning algorithm, a method of pre-clustering task airspace based on position correlation is proposed under the condition of future large-scale airspace joint operations. This method innovatively meshes the battlefield airspace, abstracts the mission airspace into battlefield obstacles while grid characterizing, and uses the characteristics of traditional A * algorithm, such as high efficiency, fast speed, high integration with grid system, and good fit, to adjust and improve the Multi A * algorithm to multiple \"start point- end point\" and synchronous path planning. In the experimental simulation part, the A * algorithm is first verified to be more suitable for solving the problem in this paper by comparison, and then two core indicators (namely, the accuracy rate of detection results and the ratio of detection speed) are set for the accuracy and efficiency that are the most important aspects of traditional airspace conflict detection, aiming to explore the stability and sustainability of airspace clustering method for airspace conflict detection. The main conclusions are as follows:  
1. The accuracy of detection results of airspace conflict detection based on airspace clustering is different from that of traditional wide area airspace conflict detection. The main reason is that there are some fuzzy disputes near the edge cluster lines in the airspace, which may lead to multiple or missing detections in the overall detection due to unclear clustering results, and ultimately affect the accuracy of detection. 2. Although there are differences in the accuracy of detection results between airspace conflict detection based on airspace clustering and traditional airspace conflict detection based on wide area, this difference can be continuously reduced by adjusting the number of airspace and other parameters, especially in the case of large-scale task airspace, the accuracy of the method proposed in this paper has been gradually improved, and the error rate is also declining. 3. Clustering the whole task airspace can effectively improve the efficiency of conflict detection. The larger the number and scale of airspace, the more time saved and the greater the speed difference between airspace conflict detection based on airspace clustering and traditional detection methods. This shows that airspace clustering is a pre preparation work when facing the challenge of large-scale airspace tasks. It has great application potential and great significance. 4. The efficiency of airspace conflict detection based on airspace clustering mentioned in point (3) is restricted by many factors. For example, when the number of airspace is limited, simply increasing the number of airspace clusters will lead to the saturation of airspace clustering, and the detection efficiency will gradually decrease. Therefore, the number of battlefield airspace and other parameters should be sorted, integrated, analyzed, and calculated in advance before airspace clustering, in this way, reasonable airspace clustering can be carried out to achieve the best operational efficiency. 5. The method proposed in this paper provides a new idea for the traditional battlefield airspace management. Such a simplified dimension reduction method greatly improves the accuracy of battlefield airspace management and also greatly releases the burden and pressure of battlefield airspace management and control. It is expected to be applied to large-scale joint operations in the future and become the key and booster of efficient and safe battlefield airspace management and control. **Author Contributions:** Conceptualization: M.C. and L.W. ; Methodology: M.C., L.W. and Z.J. ; Software: M.C., L.W. and Z.J. ; Validation: M.C. ; Investigation: L.W. ; Resources: M.C. and L.W. ; Data Curation: M.C., L.W. and D.Q. ; Writing--Original Draft Preparation: M.C. ; Writing--Review and Editing: M.C., L.W., Z.J., M.L. and Z.G.