a Screen shot of matlab code is fine

2 Extended Kalman Filter - Uncertainty Propagation Consider now a robot governed by the same motion model given in the previous exercise. Its initial pose is modeled by a multivariate Gaussian distribution with mean (0, 0,0) and covariance 0.01 0 0 ?,-| 0 0.01 0 0 0 0.01 The covariance of the state evolution noise is 0.05 0 0 R-0 0.05 0 0 0 0.08 The robot is equipped with a sensor returning the distance from two landmarks at known locations. The first landmark is at position (0,4) while the second is at (5,0). The covariance of the sensor noise is 0.01 0 Q=1 0 0.01 The robot first receives the input (0.2,0.2) and then the sensor measurement (3.9, 4.7) 1, determine the uncertainty ellipsoid (95% confidence level) after the prediction step, and com- pute its volume 2 Extended Kalman Filter - Uncertainty Propagation Consider now a robot governed by the same motion model given in the previous exercise. Its initial pose is modeled by a multivariate Gaussian distribution with mean (0, 0,0) and covariance 0.01 0 0 ?,-| 0 0.01 0 0 0 0.01 The covariance of the state evolution noise is 0.05 0 0 R-0 0.05 0 0 0 0.08 The robot is equipped with a sensor returning the distance from two landmarks at known locations. The first landmark is at position (0,4) while the second is at (5,0). The covariance of the sensor noise is 0.01 0 Q=1 0 0.01 The robot first receives the input (0.2,0.2) and then the sensor measurement (3.9, 4.7) 1, determine the uncertainty ellipsoid (95% confidence level) after the prediction step, and com- pute its volume