## 1 Introduction  
With the continuous development of space technology, maneuvering ability in real time is a key factor in completing complicated space missions for maneuvering targets [1; 2; 3; 4]. Performing accurate maneuvering target tracking and precise locating are the core issues in the field of space target surveillance [5; 6; 7; 8]. For maneuvering target tracking, the single model filter produces large filtering errors, and it is not appropriate for this application because the filtering algorithms highly depend on the motion model of the targets. The interacting multiple model (IMM) method integrates a variety of models to describe the motion rules of the targets, which improves the filtering precision. IMM was proposed by [PERSON] and [PERSON] [9], and consists of three processes: interaction, filtering, and fusion. The interaction process is the core difference between IMM and the first generation of multiple model filters, called autonomous multiple model (AMM) filters [10], which connect all the single filters organically to allow the \"soft switching\" between models. Due to the ability to automatically adjust the bandwidth of the filter, IMM has been widely used in navigation, positioning, signal processing, maneuvering target tracking, and other fields [11; 12]. Based on the standard IMM, increasingly improved IMM algorithms have been created to further increase the filtering accuracy, including the Variable Structure Interactive Multiple Model (VSIMM) [13], Adaptive Interactive Multiple Model (AIMM) [14], and the Adaptive Grid Fuzzy Multiple Model (AGFIMM) [15]. After analysis, the above algorithms are common for all dynamic adjustments of the multi-model set and for real-time conversions between multi-model sets, in order to adapt to the real-time signal change and to improve the filter's precision. However, when interacting, the weighted sum of multiple random variables will lead to multiple peaks in the probability density function of the system state, so the sum will not conform to the pure Gaussian distribution [16; 17]. As a result, the filtering precision is negatively influenced for the non-Gaussian distribution. Two methods are used to address the problem. The Gaussian distribution can be used to approximate the non-Gaussian distribution [18]. For example, [PERSON] uses the mean square error (MSE) to estimate the error covariance of the interacting random variable [19], but the estimated value is too conservative. Because the MSE estimation results are used in the filter iteration, it artificially increases the system noise in the filtering process, which makes the calculation value of gain matrix too large. Therefore, the filtering precision decreases, especially with a large measured value of noise [20]. The second method is to filter the results of the interaction by using non-Gaussian filters [21]. For instance, the IMM-PF filtering method [22; 23] can be used, but the particle filter (PF) causes problems, such as particle degradation and poor real-time performance, in engineering applications [24; 25]. The unscented Kalman filter (UKF) is the most widely used algorithm with a single filter in the IMM [26; 27; 28; 29; 30; 31; 32], and has many advantages compared to the Kalman Filter (KF), Extended Kalman Filter (EKF) [33; 34], and PF. However, the tracking accuracy decreases significantly when sensor faults occur, because model mismatches are produced when the sensor receives measurements with biases [35; 36; 37]. Generally, the sensor faults include four varieties: complete failure, fixed deviation, drift deviation, and accuracy decrease. The second and third faults are considered in this research. The current existing adaptive UKF has been proven to be effective in dealing with unknown noise, but no effective adaptive UKF algorithms exist for sensor faults and model mismatches [38; 39]. Recently, [PERSON] et al. [40] proposed the cubature Kalman filter (CKF), based on the cubature principle, and provided rigorous mathematical proof.