# Vehicle Pose Estimation Method Based on Maximum Correntropy Square Root Unscented Kalman Filter
## 5 Results
### Simulation Results and Analysis
#### 5.1.1 The Accuracy Comparison of Different Algorithms
When adding noise to the robot speed \(V\), \(\sigma_{1}^{2}=\sigma_{\rm V}^{2}=4\) m. When adding noise to the robot rudder angle \(\gamma\), \(\sigma_{1}^{2}=\sigma_{\gamma}^{2}=~{}100^{\circ}\). The simulation results are given in Figures 7-9, and \[\text{error}_{x,k}=\sqrt{\frac{1}{m}\sum\limits_{s=1}^{m}\bigl{[}\hat{x}_{k}(s)-x _{k}(s)\bigr{]}^{2}}, \tag{59}\]0 Figure 7 shows the comparison of the simulation results of five algorithms in Gaussian mixture noise. Figure 8 shows the RMSE comparison of five algorithms in Gaussian mixture noise. Figure 9 shows the state estimation errors comparison of five algorithms in Gaussian mixture noise. Table 2 shows the Average RMSE and average state estimation errors of five algorithms in Gaussian mixture noise. \[\text{error}_{x,k}=\sqrt{\frac{1}{m}\sum\limits_{s=1}^{m}\bigl{[}\hat{x}_{k}(s)-x _{k}(s)\bigr{]}^{2}}, \tag{59}\]1 RMSE of five algorithms in Gaussian noise. \[\text{error}_{x,k}=\sqrt{\frac{1}{m}\sum\limits_{s=1}^{m}\bigl{[}\hat{x}_{k}(s)-x _{k}(s)\bigr{]}^{2}}, \tag{59}\]2 State estimation errors of five algorithms in Gaussian noise. \[\text{error}_{x,k}=\sqrt{\frac{1}{m}\sum\limits_{s=1}^{m}\bigl{[}\hat{x}_{k}(s)-x _{k}(s)\bigr{]}^{2}}, \tag{59}\]3 RMSE of five algorithms in Gaussian mixture noise. \[\text{error}_{x,k}=\sqrt{\frac{1}{m}\sum\limits_{s=1}^{m}\bigl{[}\hat{x}_{k}(s)-x _{k}(s)\bigr{]}^{2}}, \tag{59}\]4 Comparison of simulation results of five algorithms in Gaussian mixture noise. \[\text{error}_{x,k}=\sqrt{\frac{1}{m}\sum\limits_{s=1}^{m}\bigl{[}\hat{x}_{k}(s)-x _{k}(s)\bigr{]}^{2}}, \tag{59}\]5 State estimation errors of five algorithms in Gaussian mixture noise. In Figure 7, we observe that the simulation results obtained by EKF-SLAM, UKF-SLAM, and SRUKF-SLAM have little difference, and there is a large error compared to the true path. Because the above three algorithms are affected by non-Gaussian noise and exhibit divergence, it increases the difficulty of control. The simulation results obtained by the MCUKF-SLAM algorithm perform better than the above three algorithms, which also proves the effectiveness of the Maximum Correntropy in the non-Gaussian noise environment. The simulation results of the proposed MCSRUKF-SLAM algorithm are optimal, and its pose estimation results are the closest to the true path. Therefore, compared to other algorithms, the proposed MCSRUKF-SLAM algorithm has higher pose estimation accuracy and better anti-interference ability in Gaussian mixture noise. In Figures 8 and 9, and Table 2, we observe that the accuracy of MCUKF-SLAM is higher than that of the EKF-SLAM, UKF-SLAM, and SRUKF-SLAM algorithms. The accuracy of the EKF-SLAM algorithm is the worst, and UKF-SLAM and SRUKF-SLAM show almost the same accuracy performance. The proposed MCSRUKF-SLAM algorithm is significantly better than other algorithms in pose estimation accuracy. Furthermore, the accuracy of the proposed MCSRUKF-SLAM algorithm improved by 81.8%, 80.9%, 78.7%, and 63.6% compared to EKF-SLAM, UKF-SLAM, SRUKF-SLAM, and MCUKF-SLAM, respectively. * Colored noise In this simulation, we set the state noise as Gaussian noise, as shown in Equation (65). The measurement noise was set as colored noise, and its distribution function [38] can be defined as \[\text{error}_{x,k}=\sqrt{\frac{1}{m}\sum\limits_{s=1}^{m}\bigl{[}\hat{x}_{k}(s)-x _{k}(s)\bigr{]}^{2}}, \tag{59}\]6 (67) where \(f_{1}=N\left(\mu,\sigma_{1}^{2}\right)\), \(c_{1}\) and \(c_{2}\) are weight coefficients, and \(c_{1}=0.8\), \(c_{2}=-0.6\). The value of \(\sigma_{1}^{2}\) in f1 refers to Equation (66). The simulation results are given in Figures 10-12 and \[\text{error}_{x,k}=\sqrt{\frac{1}{m}\sum\limits_{s=1}^{m}\bigl{[}\hat{x}_{k}(s)-x _{k}(s)\bigr{]}^{2}}, \tag{59}\]7 Figure 10 shows the comparison of the simulation results of five algorithms in colored noise. Figure 11 shows the RMSE comparison of five algorithms in colored noise. Figure 12 shows the state estimation errors comparison of five algorithms in colored noise.