Approximate Kalman filtering by both M-robustified dynamic stochastic approximation and statistical linearization methods

Krylov space approximate Kalman filtering

The Kalman filter is a technique for estimating a time-varying state given a dynamical model for, and indirect measurements of, the state. It is used, for example, on the control problems associated with a variety of navigation systems. Even in the case of nonlinear state and/or measurement models, standard implementations require only linear algebra. However, for sufficiently large-scale probl...

Linearization of RF Power amplifiers Using Adaptive Kalman Filtering Algorithm

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A Statistical Mechanical View of Kalman Filtering

We develop a physical analogy for the Kalman-Bucy filter, in which the partially observed signal describes a statistical mechanical system interacting with a heat bath. The ‘universe’ comprising this system and the heat bath obeys a non-decrease law of entropy similar to the Second Law of Thermodynamics. However, this law can be violated in the presence of partial observations. Like Maxwell’s d...

Kalman Filtering with Statistical State Constraints

For linear dynamic systems with white process and measurement noise, the Kalman ̄lter is known to be the minimum variance linear state estimator. In the case that the random quantities are Gaussian, then the Kalman ̄lter is the minimim variance state estimator. However, in the application of Kalman ̄lters known signal information is often either ignored or dealt with heuristically. For instance...

Extended Kalman Filtering by