# Clustering Method of Large-Scale Battlefield Airspace Based on Multi A * in Airspace Grid System
## 1 Introduction  
The English prototype of the word \"Airspace\", which is defined in the Oxford Dictionary as \"the part of the sky where planes fly, usually the part above partial country that is legally controlled by that country\". Here, airspace is literally translated into the air space of aircraft flight, which also makes the academic world view that airspace as equivalent to the air space above the land [1, 2]. The International Civil Aviation Organization (ICAO) also divides airspace into seven categories according to different airworthiness rules and flight conditions of aircraft, namely, A, B, C, D, E, F, and G [3]. However, as an important public natural resource and carrier of air transportation, airspace resources not only have certain economic value and public service functions [4], but also are core strategic resources [5, 6, 7]. It is an important carrier to realize various complex combat tasks [8, 9, 10, 11], and it is also the support and guarantee of various combat operations [12]. Only with efficient and smooth battlefield airspace control capability can we truly master the lifeline to win the battlefield. The airspace mentioned in this paper mainly refers to the middle- and high-altitude airspace above the battlefield. In this narrow airspace, there are often dense airspace users ranging from various artillery shells, rockets, and missiles to unmanned aerial vehicles, helicopters, fighter jets, bombers, and other airspace users. These factors are converging, leading to an increasingly complex battlefield airspace environment, and the probability of accidental injury and collision between airspace users is greatly increased; it seriously restricts the improvement of airspace control efficiency and combat effectiveness. In order to simplify the control area and reduce the difficulty of control, inspired by the concept of an \"airspace control sector\" [13, 14, 15, 16] in civil aviation, this paper takes advantage of the unique advantages of the airspace grid system [17, 18, 19, 20] in an airspace grid representation and time-space binary computing to divide the vast battlefield airspaceinto multiple mission airspace clusters based on appropriate segmentation criteria. The goal is to reduce the burden of airspace control through airspace clustering control and improve the accuracy and efficiency of battlefield airspace control. Through experimental comparison, it is verified that the A * algorithm [21; 22; 23; 24] is more efficient and faster than other traditional path planning algorithms in generating clustering edge lines, and it has a high degree of integration and better fit with the grid system. At the same time, the key link of airspace conflict detection [25] in airspace management and control is selected as the experimental object, and the speed and accuracy of airspace conflict detection before and after clustering are compared according to the experimental simulation to evaluate the role of airspace clustering in improving the overall effectiveness of airspace management and control. The experiment shows that after clustering the whole airspace, the detection efficiency can be improved by at least 1.6% and at most 30.25% on the premise of ensuring the detection accuracy. The following contents are organized as follows: Section 2 analyzes the key issues of airspace association clustering, as well as the possibility and feasibility of organic integration of current battlefield airspace clustering and path planning intelligent algorithm, and combs out the basic process of airspace cluster division based on Multi A * under grid system; In Section 3, the theoretical and technical foundations of airspace reference grid generation system and airspace sector division are introduced; Section 4 introduces the method in detail, and gives an example calculation for each step. In Section 5, the experimental simulation is carried out under the background of the assumed task and preliminary conclusions are obtained.