## 1 Introduction  
Maneuvering target tracking is a fundamental and critical task in many practical applications with a wide range of military and civil backgrounds [1; 2]. Due to the inconsistency of maneuverability in all directions in three-dimensional space, the target motion cannot be accurately described by the expansion of a general two-dimensional model and method, especially for the high-speed and high-maneuvering targets. Therefore, three-dimensional high-maneuvering target tracking has now become one of the difficulties in this kind of problem [3; 4; 5; 6]. For high-maneuvering target tracking, the establishment of a motion model is the primary task. So far, the multiple-model (MM) methods [7; 8; 9; 10] are usually adopted because of their fine modeling. However, such methods need to add as many motion models as possible to ensure the completeness of the model set, which will lead to a significant increase in computational complexity and unnecessary competition among similar models. Besides, the MM methods rely heavily on the priori assumptions, such as the Markov transition matrix. Another method is to assume that the motion model is fixed, and that the maneuver is a disturbance caused by an injection of maneuver noises into our model [11]. Through different assumptions about the statistical characteristics of noises, researchers proposed many different models, among which the [PERSON] model [12], the semi-Markov model [13] and the \"current\" statistical model [14] are widely used. However, these models only consider the acceleration maneuver of a target. For high-order maneuvers, [PERSON] proposed the Jerk model [15; 16]. This model achieves high accuracy when tracking high-speed and high-maneuvering targets, but it has some steady-state deterministic error in tracking the step acceleration change rate signal. Considering the limitations of the Jerk model, [PERSON] et al. drew on the idea of the \"current\" statistical model and proposed a CS-Jerk model that adapts to high-maneuvering targets [17; 18]. Compared with the Jerk model, this CS-jerk model has a better tracking performance for high-speed and high-maneuvering targets. Therefore, in our study, the CS-Jerk model is selected as the motion model to solve the three-dimensional high-maneuvering target tracking problem. In target tracking, the filtering algorithm is the most critical step, which directly affects tracking performance. Owing to the adaptability in strong nonlinear non-Gaussian systems, Particle Filter (PF) is often used [19; 20; 21]. However, the PF scheme, of which the complexity depends largely on its sample size, would suffer particle degeneracy. Another new solution is based upon artificial intelligence methods, such as combining a general regression neural network (GRNN) as our tracking algorithm with a multiple-model method [22; 23; 24]. This method can deal with non-linear problems well, but its real-time performance is poor [25]. When the target motion changes a lot, the network needs to be retrained in order to adapt to the new motion mode. Therefore, the current mainstream tracking algorithm research is still based on the PF scheme, and on this basis to make improvements. There are usually three ways to improve: Choosing proper proposal distribution, resampling to obtain a high-quality sample set and introducing an effective sample size. Based on the first point, [PERSON] proposed the unscented particle filter (UPF) [26], which uses the mean and variance of the particle state obtained by an unscented Kalman filter (UKF) to approximate the importance density function. Compared with general PF and UKF, UPF achieves higher accuracy while bringing about higher computational complexity, and it will also be troubled by particle degeneracy when the proposal distribution deviates too far from the real state [27; 28; 29].