The Geometrically Intrinsic Nonlinear Recursive Filter, or GI Filter, is designed to estimate an arbitrary continuous-time Markov diffusion process X subject to nonlinear discrete-time observations. The GI Filter is fundamentally different from the much-used Extended Kalman Filter (EKF), and its secondorder variants, even in the simplest nonlinear case, in that: ¥ It uses a quadratic function o...

There are several functional forms for non-linear dynamical filters. Extended Kalman filters algorithms that used to estimate more accurate values of unknown quantities internal systems from a sequence noisy observation measured over period time. This filtering process becomes computationally expensive when subjected high dimensional data which consequently has negative impact on the filter per...

For linear dynamic systems with white process and measurement noise, the Kalman filter is known to be an optimal estimator. In the application of Kalman filters there is often known model or signal information that is either ignored or dealt with heuristically [1]. This work presents a way to generalize the Kalman filter in such a way that known relations among the state variables (i.e., state ...

In this paper, a decentralized information filter in federated structure is developed in multi-sensor environments. Being equivalent to the Kalman filter algebraically, the information filter of Mutambara 11) is extended to N -sensor distributed dynamic systems. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KFbased filter. The ...