## 1 Introduction  
A hypersonic glide target (HGT) [1] has a flight Mach number greater than 5. It has the characteristics of a fast flight speed, strong penetration capability, strong mobility, and high accuracy. It adopts an aerodynamic shape with a large lift-drag ratio, and its trajectory is complex and changeable, so it is difficult to achieve effective defense. Therefore, tracking and locating the target have always been difficult and hot issues in the field of target tracking. With the continuous deepening of the research on the characteristics of maneuvering targets, experts and scholars have proposed a variety of single-model motion models to fit maneuvering targets: [PERSON] et al. proposed the constant velocity (CV) model and constant acceleration (CA) model in 1973 [2]. The Singer model, also known as the time-dependent model, was first developed by [PERSON] and proposed in 1970 [3]. Professor [PERSON] of China put forward the CS model in the 1980s [4]. In 1997, Indian scholar [PERSON] proposed the Jerk model [5], with reference to the [PERSON] model, expanded the dimension of traditional acceleration, and proposed the concept of acceleration. Some experts and scholars have improved these models alone, but for highly maneuvering targets such as hypersonic targets, it is difficult for a single model to match their motion states. When the difference between motion states is large, it will lead to filtering divergence and tracking failure [6]. To solve the above problems, in 1965, [PERSON] proposed multiple models (MM) [7] to improve the limitation of single models. On the basis of the multi-model algorithm, [PERSON] and [PERSON] proposed the IMM algorithm by using the pseudo-Bayesian algorithm, and used the Markov transformation matrix to overcome the filtering divergence caused by the fixed model [8]. It tracks the uniform motion process, significantly reduces the noise, and has high tracking accuracy and a fast convergence speed [9]. Since the IMM algorithm was proposed, scholars have applied it to the target tracking field and conducted a significant amount of research. For the tracking of hypersonic gliding targets, [PERSON]. [10] proposed a self-adaptive algorithm of maneuvering frequency in the Singer model, which uses filtering information to calculate and adjust the maneuvering frequency in the Singer model. Compared with the IMM Singer algorithm, it has high tracking accuracy and requires a small amount of calculation. The nonlinear filtering algorithm plays an important role in the field of target tracking, such as multi-base radar positioning [11] and nonlinear filtering vehicle adaptive navigation [12]. Since the measurement model of hypersonic target radar is also nonlinear, [PERSON]. [13] proposed the use of the unscented Kalman filter (UKF) [14] combined with the IMM algorithm to realize the tracking filtering of hypersonic targets. For the problem of model interaction, [PERSON]. [15] proposed an improved AIMM algorithm with Markov parameters. The tracking accuracy is improved, but it is only applicable to slow maneuvering targets with prior information. On this basis, [PERSON]. [16] proposed an adaptive variable structure interactive multi-model (AVSIMM), which improves the tracking accuracy of hypersonic targets. To sum up, there are three main problems in the IMM tracking algorithm for highly maneuvering targets such as hypersonic targets: The first is, the selection of models. The conventional CV, CA, and other models are weak in mobility, and cannot match the motion state of hypersonic targets. The second is the filtering algorithm. Most of the measurements of hypersonic targets are nonlinear measurement equations, which are not suitable for the linear Kalman filter (KF) in the IMM model; at the same time, the accuracy of the filtering algorithm needs to be improved. The third is the model interaction problem. The Markov matrix of the conventional IMM algorithm is fixed, the model probability interaction adaptability is weak, and the matching accuracy is low.