## 1 Introduction  
Hypersonic glide vehicle (HGV) [1] is a rapid strike weapon which can make long-range glide in near space by aerodynamic force at greater than Mach 5. It is a great threat to world peace that HGV can even strike anyplace in the world within two hours. Therefore, some papers about HGV tracking are published for the research of defending this weapon. The wide range of maneuvers in three-dimension makes it really difficult to track the HGV and researchers prefer multiple model algorithms to single model algorithms for tracking the so-called strong maneuvering targets. Though the study of HGV tracking is in primary stage, there are still some representative papers about tracking some simple but not actual HGV trajectory. Zhang et al. [2] study the HGV's Sanger trajectory [3] (without lateral maneuver) tracking using interacting multiple model (IMM) [4] algorithm based on constant acceleration (CA) model, constant turning (CT) [5] model and Singer model. Qin et al. [6] study the near space target tracking with adaptive grid interacting multiple model (AGIMM) [7] algorithm and the target trajectory is a simple combination of constant-velocity motion, constant-turning motion and constant-acceleration motion. Furthermore, the AGIMM with three different CT models is used to track the near space target, which is just called near space target but does not consist of any character of HGV. Zhai et al. [8] use adaptive IMM algorithm based on the dynamic model to study the tracking and predicting the HGV trajectory with the fixed known attack angle and the varying unknown bank angle. The attack angle and the bank angle are the two control variables. However, in real HGV trajectory tracking, the attack angle and the bank angle are unknown, which is different from all mentioned above. Thus, tracking the maneuvering trajectory with unknown attack angle and unknown bank angle will be studied in this paper using several algorithms. If we track HGV just using some kinematic models, such as CA model, constant velocity (CV) model, CT model and so on, we may miss much important HGV information. To develop new HGV tracking algorithms using as much information as possible, in every model filter, we establish the process equation based on HGV aerodynamic model and the measurement equation according to radar measurement principle. However, the process equations of the two control variables are unknown by defenders. Hence, an effective method is needed to be proposed for modeling the rates of change of the two control variables. Furthermore, if we do not know how the two control variables change from the discrete time k-1 to k, we can also use multiple model tracking algorithms, including IMM algorithm, AGIMM algorithm and HGMM [9] algorithm. We will use different algorithms mentioned above to track the maneuvering trajectory with unknown attack angle and bank angle, and compare the accuracy and computational cost in the whole tracking stage. Cubature Kalman filter (CKF) [10] is proposed by Arasaratnam based on the Cubature rules with rigorous mathematical derivation. CKF is more accurate than extended Kalman filter (EKF) [11] in nonlinear filter and more accurate than unscented Kalman filter (UKF) [12] in high-degree nonlinear system filter [13]. Furthermore, the system of tracking HGV is a high-degree nonlinear system. According to the above considerations, every model filter in algorithms in this paper can be constructed using CKF. Radar is measurement equipment widely used in object detection and target tracking. In this paper, we conduct HGV tracking using only one sensor namely radar and in the future we could conduct HGV tracking using multiple sensors because of more measurement information and higher accuracy. If we used multiple sensors to measure HGV, the fuzzy multiple-sensor data fusion Kalman filter approach [14] proposed by James A. Rodger can improve the tracking performance effectively, which will be studied in the future. This paper is organized as follows.