# State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF
###### Abstract  
The prerequisite for intelligent vehicles to achieve autonomous driving and active safety functions is acquiring accurate vehicle state parameters. Traditional Kalman filters often under-perform in non-Gaussian noise environments due to their reliance on Gaussian assumptions. This paper presents the IMM-MCCKF filter, which integrates the interacting multiple model theory (IMM) and the maximum correntropy cubature Kalman filter method (MCCKF), for estimating the state parameters of intelligent vehicles. The IMM-MCCKF successfully suppresses non-Gaussian noise by optimizing a nonlinear cost function using the maximum correntropy criteria, allowing it to capture and analyze signal data outliers accurately. The filter designs various state and measurement noise submodels to adapt to the environment dynamically, thus reducing the impact of unknown noise statistical properties. Accurately measuring the velocity of a vehicle and the angle at which its center of mass drifts sideways is of utmost importance for its ability to maneuver, maintain stability, and ensure safety. These parameters are critical for implementing advanced control systems in intelligent vehicles. The study begins by constructing a nonlinear Duggoff tire model and a three-degrees-of-freedom (3 DOF) vehicle model. Subsequently, utilizing low-cost vehicle sensor signals, joint simulations are conducted on the CarSim-Simulink platform to estimate vehicle state parameters. The results demonstrate that in terms of estimation accuracy and robustness in non-Gaussian noise scenarios, the proposed IMM-MCCKF filter consistently outperforms the MCCKF and CKF algorithms. vehicle state estimation; maximum correlation entropy cubature Kalman filter; interacting multiple model algorithm; non-gaussian noise +
Footnote †: journal: Applied  
0000-0001-9993-3889]C. [PERSON]  
0000-0002-3330-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-33000-3300-3300-3300-33000-33000-33000-33000-33000-33000-33000-33000  
the vehicle's actual working environment's complexity and noise uncertainty, estimation accuracy and issues such as divergence are reduced. Estimating vehicle status parameters using the Kalman filter [9] and its improved algorithm is standard practice. To estimate the parameters of an electric tram's servo control system, [PERSON] et al. [10] suggested an enhanced SAKF. The capacity to detect uncertain disturbances is improved by incorporating adaptive fuzzy clustering technology. Simulations have shown that it offers higher estimation accuracy and robustness than traditional EKF. [PERSON] et al. [11] developed a fault-tolerant EKF that can adapt to the loss of sensor input and still estimate the vehicle's status. Simulations have demonstrated superior adaptability to sensor data loss and parameter disturbances under complex driving conditions compared to the traditional EKF filter. To overcome the limitations of EKF in handling nonlinear systems, scholars have proposed the UKF arithmetic [12]. UKF avoids the Taylor expansion and linearization of nonlinear functions by selecting Sigma points, eliminating the need to compute Jacobian matrices and avoiding truncation errors. Selecting Sigma points helps capture the system's nonlinear relationships better, improving state estimation accuracy. [PERSON] et al. [13] proposed an enhanced AUKF. It enhances the noise estimator by modifying the forgetting factor, leading to a more precise estimation of vehicle state parameters. [PERSON] et al. [14] proposed an EKF-based adaptive vehicle state estimator that uses a dynamic vehicle model to calculate the yaw rate, longitudinal and lateral speeds, and other relevant parameters. According to the experimental results, the proposed technique is effective under typical and perturbed vehicle characteristics. To overcome the limitations of UKF in processing high-dimensional nonlinear systems, [PERSON] et al. [15] utilized the spherical-radial volume rule to implement a CKF.