# State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF
###### Abstract
It avoids issues that may arise with UKF in high-dimensional settings and demonstrates superior performance in complex system state estimation. [PERSON] et al. [16] proposed an RCKF that improves vehicle estimation performance by enhancing the robustness of the Kalman filter. [PERSON] et al. [17] proposed a novel high-order CKF method and applied it to state estimation in quadrotor UAVs. Simulation results show that the HDCKF provides higher estimation accuracy while maintaining low computational complexity, making it well suited for real-time state estimation in high-dimensional nonlinear systems. In addition to traditional filtering algorithms, other researchers have explored integrating deep learning techniques to estimate vehicle state parameters efficiently. [PERSON] et al. [18] suggested an adaptive filter that utilizes dual deep neural networks for vehicle state estimation. This method utilizes deep learning capabilities to enhance the adaptive filtering process, offering a more accurate and robust alternative in dynamic and complex environments, thus demonstrating the potential application of deep learning in vehicle state estimation. To enable intelligent vehicles to monitor and manage vehicle states more comprehensively, analyzing battery state parameters has become particularly important, in addition to estimating dynamic parameters. [PERSON] et al. [19] introduced a technique using exogenous functions to determine the parameters of vehicle batteries in V2G systems. This approach integrates real-time estimation with actual measurement data, thus improving the system's robustness and accuracy. Most current algorithms for vehicle state parameter estimation research assume that noise follows a Gaussian distribution. However, in practical applications, sensors are susceptible to external environmental interference, leading to anomalous data in measurements. Noise near these anomalies exhibits heavy-tailed distribution characteristics, reflecting non-Gaussian noise properties [20; 21; 22]. As a result, researchers have suggested enhancing Kalman filtering's estimation effectiveness in extreme conditions by combining it with the MCC [23; 24; 25; 26; 27]. In non-Gaussian situations, the MCCKF is more appropriate for state estimation of high-dimensional nonlinear systems because it combines the cubature Kalman algorithm with the maximum correntropy criterion. In practical applications, due to the variable road conditions faced by vehicles, relying on a single model for state estimation often fails to accurately capture the operational state of the car under different working conditions [28]. To address this issue, scholars have proposed the interacting multiple model (IMM) theory, which allows multiple models with different characteristics to operate in parallel [29]. Location and status estimation systems for vehicles have extensively used this technique [30], enabling state estimation filters to adapt more flexibly to changes in the noise environment, thereby increasing the accuracy and robustness of state estimation. [PERSON] et al. [31] proposed an IMM-RUKF algorithm for all-terrain off-road ground target tracking. This algorithm performs better in tracking highly maneuverable targets across various scenarios than a single model. Notably, the MCC is integrated within the KF framework. The CKF reduces computational complexity and enhances estimation stability in high-dimensional nonlinear systems compared to the UKF. Hence, this study selects CKF as the framework for deriving the MCCKF filtering algorithm. Considering the unknown statistical noise characteristics in practical applications, this study introduces the IMM theory, combining MCCKF with IMM to propose the IMM-MCCKF algorithm for high-accuracy estimation of vehicle state parameters. This paper makes the following contributions: (1) The MCCKF algorithm is derived from MCC and CKF. Then, the IMM theory is introduced, leading to the derivation of the IMM-MCCKF algorithm.