# Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment
## 2 Related Works
### Vehicle Motion States Estimation  
The estimation of vehicle motion states is a hot topic of current studies. Even though the relative research mainly focuses on a traditional Ackerman turning vehicle, the estimation algorithm for the Ackerman turning vehicle is still useful for a skid-steered wheeled vehicle.  
The Kalman filter (KF) is the most widely used observer for estimating vehicle states; however, the traditional KF is only adopted in linear driving conditions. Thus, nonlinear KF improved algorithms have been adopted for estimating vehicle states. [PERSON] [24] established the Luenberger observer for a priori estimates, and then used EKF for a posteriori observer based on multi-information fusion. [PERSON] [25] used EKF to estimate vehicle states based on a vehicle dynamic model and [PERSON] [26] used EKF to estimate tire force of three axes. [PERSON] [27] combined the auxiliary particle filtering (APF) and iterated expanded Kalman filtering (IEKF) to estimate the road adhesion coefficient and sideslip angle of tires. [PERSON], [PERSON] [28] developed an adaptive UKF to estimate vehicle states on different roads. [PERSON] [29] established a cubature Kalman filter to estimate kinetic states for a distributed electric vehicle based on the 3-DOF dynamic model and Duggoff tire model. [PERSON] [30] used a UKF-based adaptive variable structural observer with dynamic correction (AUKF) for vehicle body sideslip angle. [PERSON] [31] developed a multi-innovative unscented Kalman filter (MI-UKF) based on an ICR kinetic model of a skid-steered robot. As compared with EKF based on Taylor expansion to realize linear approximation, the UKF performed more accurately in high nonlinear work conditions [32]. To improve the performance of the traditional UKF, some improved algorithms for UKF have been proposed. The strong tracking algorithm [30,33] was proposed to solve the problem that the UKF prediction matrix may not be positive in some conditions. Considering that a variable environment leads to a variable noise matrix, an adaptive variance matrix adjustment was proposed to improve the accuracy and stability of the UKF [34, 35, 36].  
The Kalman filter is based on a Gaussian assumption. To process non-Gaussian conditions, the partial filter (PF) based on importance sampling and Monte Carlo assumption was proposed and was applied in vehicle state estimation and location [37,38]. The main problem in the PF is particle degradation [39]. The unscented partial filter (UPF) establishes particle swarm using unscented transformation similar to the UKF to avoid particle degradation [40]. [PERSON] [41] and [PERSON] [42] estimated electric vehicle state using the UPF. [PERSON] [43] built a modified PF which performed better than the UPF in the sensor fault mode. Although the UPF performs better than the UKF, in some studies in the literature, in high-nonlinear and non-Gaussian conditions, the larger amount of computation than the UKF limits its application on real vehicles. The UPF has not been widely applied in real vehicle state estimation [44,45] and the reliability of the UPF has not been efficiently verified in real vehicle state estimation as compared with the UKF algorithm.