# Clustering Method of Large-Scale Battlefield Airspace Based on Multi A * in Airspace Grid System
## 2 Analysis of Key Problems  
This paper plans to break through the traditional battlefield airspace management and control mode in two aspects: the method and technology of battlefield airspace management and control. On the technical level, in recent years, grid technology [17; 19; 20] has become an emerging research field. On the basis of the grid division system [18; 26; 27], experts and scholars have designed corresponding geo-airspace data organization methods [28; 29] and generated unique grid structures [30]. Because of its unique advantages in airspace grid representation and space-time binary computing [26; 31], it has been widely used in the field of airspace model representation and establishment. For a long time, the civil aviation field has been faced with the problem of sharp contradiction between the demand and supply of airspace resources and great pressure on air flow control. In order to reasonably allocate and plan airspace resources and improve public traffic management capabilities, the relevant departments of air traffic control have introduced the concept of an \"airspace control sector\" [13; 14], which has improved the safety and efficiency of airspace operations, and it has also provided a new idea for the efficient control of battlefield airspace in blocks. As a special airspace, the battlefield airspace has the characteristics of narrower airspace scope and more concentrated activity space compared with the ordinary airspace. In the process of battlefield airspace management and control, airspace management and control personnel will not only participate in the whole process, but also spread evenly in every corner of the battlefield, without complex factors such as traffic flow relationship and controller load. Therefore, the geometric division method [32; 33; 34; 35] may be more suitable for battlefield airspace segmentation. If the battlefield airspace is projected to the geodetic two-dimensional plane, the problem of airspace segmentation will be transformed into a problem of two-dimensional image segmentation. The so-called image segmentation refers to dividing the image into several disjoint regions according to the characteristics of gray, color, airspace texture, geometric shape, etc., so that these features show consistency or similarity in the same region, and show obvious differences between different regions, at present, there are many mature image segmentation algorithms, such as threshold based segmentation methods [36; 37; 38; 39], edge detection based segmentation methods [40; 41], and image segmentation methods based on wavelet analysis and wavelet transform [42]. However, these methods can only separate the task airspace from the \"background\" environment, but they cannot divide one or more eligible airspace into an airspace cluster according to different task instructions. Due to the poor adaptability between traditional image direct segmentation methods and battlefield airspace clustering targets, this paper considers adopting an indirect airspace clustering method of \"point-line-cluster\"; that is, first find the points on the edge line of the airspace cluster, and then connect the points to form the edge line of the airspace cluster. Finally, these edge lines of the airspace cluster interweave with each other, and the entire battlefield airspace is divided into multiple clusters. Following this idea, the task airspace is regarded as an obstacle in this paper, and the airspace clustering problem is successfully transformed into a path planning problem in the free airspace under the known obstacle environment (task airspace) in the airspace grid system. At present, there are also many intelligent path planning algorithms, such as A * algorithm [22; 23], D * algorithm [43], fast expanding random tree algorithm [44], and random road map algorithm. These algorithms have their own advantages and disadvantages. A * and D * algorithms have high adaptability to the grid system.