## 1 Introduction
In other words, compared with the existing methods, this method can approximate the original trajectory with fewer trajectory points but without losing the accuracy. It will bring another potential advantage, i.e., under a certain trajectory compression rate level, the trajectory points retained by this method can better reflect the characteristics of the original trajectory, and the compressed trajectory is more similar to the original trajectory. In this way, the subsequent trajectory clustering analysis and motion feature mining will attain better accuracy. A moving target trajectory segmentation and compression algorithm based on particle swarm optimization is proposed, which is named the PSOTSC algorithm. The general idea of the algorithm is to transform the trajectory data processing problem into a global optimization problem by using the swarm intelligence method. The prominent feature of the algorithm is to establish a mapping relationship between particles and trajectories, which gives full play to the advantages of the particle swarm optimization algorithm for global search and makes it easy to obtain the optimal solution. On this basis, a new particle update strategy for fast optimization of trajectory segmentation solution is designed and named the neighborhood adjustment and random jump (NARJ). Based on this strategy, high-quality particles can evolve into better particles faster, while low-quality particles can change in time to try new segmentation possibilities, which can not only improve the overall efficiency of the algorithm, but also prevent the algorithm from falling into local search. The basic steps of PSOTSC are as follows. First, a trajectory segmentation optimization framework based on PSO is established. Second, the algorithm starts the search and optimization of trajectory segmentation solution, which involves many key processes, including initialization of particles, definition of particle fitness function based on fitting degree of trajectory profile and updating based on NARJ. Finally, each trajectory in the data set completes the optimal segmentation, which realizes the overall data compression. Hurricane real trajectory data set, ungulate location real data set and maneuvering target simulation trajectory data set [68] are used to verify the effectiveness of the algorithm. The comparative experiments with DP, TRACLUS and TCSS algorithms can prove the advantages of the PSOTSC algorithm. In summary, the contributions of this paper are as follows:  
1. We propose a technical route based on swarm intelligence to solve the problem of trajectory segmentation and compression, and few similar studies are currently available. The heuristic search and optimization of the trajectory segmentation solution based on population behavior can quickly and accurately find the global optimal segmentation feature point set without user intervention, which lays the foundation for the application of intelligent optimization algorithm in moving target trajectory data processing. 2. The proposed algorithm based on PSO has the advantage of global segmentation and compression and does not need to adjust the threshold parameters repeatedly. Compared with the local forward segmentation and compression methods, our algorithm is more accurate on the feature points selection, and the compressed trajectory has better similarity with the original trajectory. 3. We propose a new NARJ particle update strategy, which can not only make the local optimal segmentation solution evolve into a better solution efficiently, but can also make the bad solution update quickly to try new segmentation possibilities. Compared with the classical particle swarm optimization method, the generation efficiency of the optimal solution is improved. 4. We define the trajectory profile fitting degree as the similarity evaluation index between the compressed trajectory and the original trajectory and apply it to segmentation solution optimization.