Comparison of Kalman Filter Estimation Approaches for State Space Models with Nonlinear Measurements

The Extended Kalman Filter (EKF) has long been the de-facto standard for nonlinear state space estimation [11], primarily due to its simplicity, robustness and suitability for realtime implementations. However, an alternative approach has emerged over the last few years, namely the unscented Kalman filter (UKF). This filter claims both higher accuracy and robustness for nonlinear models. Several papers have investigated the accuracy of UKF for nonlinear process models, but none has addresses the accuracy for nonlinear measurement models in particular. This paper claims to bridge this gap by comparing the performance of EKF to UKF for two tracking models having nonlinear measurements.