Iterative Unbiased Converted Measurement Kalman Filter for Target Tracking

It is important to develop a robust and fast tracking algorithm for modern tracking system because the target moves more and more fast. Therefore, a novel algorithm named the iterative unbiased converted measurement Kalman filter (IUCMKF) is proposed based on the analysis and comparison of conventional target tracking methods. The new algorithm is developed from unbiased converted measurement Kalman filter, but it can obtain more accurate state and covariance estimation. Compared with the existing target tracking approaches, the proposed method has potential advantages in tracking accuracy. The correctness as well as validity of the algorithm is demonstrated through numerical simulation results.