Risk-sensitive filters for recursive estimation of motion from images

In this paper, an extended risk-sensitive filter (ERSF) is used to estimate the motion parameters of an object recursively from a sequence of monocular images. The effect of varying the risk factor θ on the estimation error is examined. The performance of the filter is compared with the extended Kalman filter (EKF) and the theoretical Cramer-Rao lower bound. When the risk factor θ and the uncertainty in the measurement noise are large, the initial estimation error of the ERSF is less than that of the corresponding EKF The ERSF is also found to converge to the steady state value of the error faster than the EKF. In situations when the uncertainty in the initial estimate is large and the EKF diverges, the ERSF converges with small errors. In confirmation with the theory, as θ tends to zero, the behavior of the ERSF is the same as that of the EKF