Proceedings PaperTracking points within noisy images
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The paper derives an optimal fixed interval smoother for the linear time invariant output estimation problem. The smoother employs forward and adjoint Kalman predictors. The suboptimal time-varying case is discussed which includes the use of forward and adjoint extended Kalman predictors within a nonlinear smoother. The efficacy of the smoother to tracking points within sequences of noisy images is investigated. The results of a simulation study are presented in which it is demonstrated that a Kalman filter can outperform a so-called matched filter, and a fixed interval smoother can provide a further performance improvement when the images are sufficiently noisy.