## 1 Introduction  
With the continuous development of global positioning technology, wireless communication technology and the wide application of mobile terminals, enormous volumes of mobile target trajectory data have appeared. Trajectory data contains rich information of target space-time characteristics. Through data mining and depth analysis, we can find high-value information, such as target activity law, behavior characteristics, interest habits, abnormal changes [1, 2, 3, 4, 5, 6], etc. However, the rapid growth of trajectory data also brings many challenges to the data service based on the target space-time location, including the increase in data transmission load, the pressure of data storage, the reduction of data query efficiency and the decline of data analysis performance [7]. Trajectory data compression is an effective way of solving the above problems, and the main way to achieve compression is to segment the trajectory, i.e., to obtain trajectory segments with uniform and homogeneous internal motion behavior or semantic characteristics based on certain partition criteria. The compressed trajectory only stores feature points and segment feature information, which can not only meet the user's requirements of similarity with the original trajectory, but also reduce the amount of data storage. The existing methods of trajectory data compression can be divided into three categories [8, 9, 10, 11]. The first is a trajectory data compression based on line segment simplification [12, 13, 14, 15, 16, 17]; the trajectory in free space is segmented linearly by constraints, and only two end points of each approximate segment are stored to achieve compression. The second is a trajectory data compression based on road network structure [18, 19, 20, 21, 22]; the trajectory points are mapped onto the road network, and the original trajectory is represented by a grid structure to reduce the amount of data. However, this kind of method is not suitable in a scenario where there is a lack of road network information, such as at sea and in the air, and it is also difficult to apply to the ground target with high degree of motion freedom. The last method is a trajectory data compression based on semantic information [23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. Semantic features are extracted from the trajectory data, and the trajectory is transformed into a semantic sequence. However, this kind of method often ignores the movement characteristics of the target, and it is difficult to obtain the semantic features of the trajectory. In our application, the main reason for trajectory data compression is that the long-term accumulated target-historical trajectories occupy a lot of storage resources, and the unsimplified data set is very difficult to use in the deep mining of target motion features. Because a large number of different types of sensors have been deployed for target reconnaissance, the data sampling rates of these sensors are generally different, and some may be very frequent, such as radar gaze tracking of targets. Therefore, the same target may get multiple trajectories, and the data of one trajectory may be very dense, so there is a lot of information redundancy in the whole database. At the same time, most of the targets we focus on are free moving targets on land, sea and air. Their activities often do not follow the road network, and it is difficult for us to know the intention of the activities, i.e., the trajectory semantic information is unknown. Therefore, among the three methods above, the trajectory data compression based on line segment simplification is the most suitable in our application, and it is also the most popular one, because it has a clear technical route, simple method, easy implementation and wide application. Therefore, the method proposed in this paper belongs to the line segment simplification type, and the following analysis is mainly focused on the research of this kind of method.