Kalman Filtering has been successfully applied to various state estimation problems in mobile robotics. This paper presents the application of Kalman Filtering to a scenario where the measurements do not come as a time series. There is also no readily available estimation matrix for the system. The advantages and limitations of using Kalman Filtering in such a scenario are discussed. The work d...

State estimation algorithm deals with recovering some desired state variables of a dynamic system from available noisy measurements, and estimation of the state variables is one of the fundamental and significant problems in control and signal processing areas, and many significant progresses have been made in this area. In 1940s, Wiener, the founder of the modern statistical estimation theory,...

This paper is a tutorial survey which focuses on some developments introduction in statistical filtering achieved since the of Wiener and Kalman filters for linear gaussian problems. Kalman are reviewed with filters (including reference to their smoothers and predictors) interesting properties and also their fundamental limitations in nonlinear or unknown environments. For nonlinear filtering p...

Estimation problems for linear time-invariant systems with noisy input and output are considered. The smoothing problem is a least norm problem. An efficient algorithm using a Riccati-type recursion is derived. The equivalence between the optimal filter and an appropriately modified Kalman filter is established. The optimal estimate of the input signal is derived from the optimal state estimate...