Multisensor Distributed Information Fusion White Noise Wiener Deconvolution Estimator

Multisensor Distributed Information Fusion White Noise Wiener Deconvolution Estimator

Multisensor distributed information fusion white noise wiener deconvolution estimator is presented in this paper. The algorithm is using the modern time series analysis method and white noise estimator under the linear minimum variance optimal fusion criterion. Gevers-Wouters (G-W) algorithm are also used in this paper. This paper presents information fusion algorithm including scalar weighted ...

Multisensor Distributed Fusion Wiener Deconvolution Estimator for Linear Stochastic Multichannel ARMA Signal

Multisensor distributed fusion Wiener deconvolution estimator is presented in this paper. It does not need to solve the Diophantine equation, and the steady-state Kalman filter gain of the augmented system. It can handle processing of nonstationary signal. White noise estimator and A strom predictor are used in the algorithm. Gevers-Wouters (G-W) algorithm is also used in this paper. In order t...

Distributed Multisensor Fusion

Lucy Y. Pao Northwestern University Evanston, IL 60208 Abstract There have been several algorithms proposed for multisensor tracking of multiple objects using a centralized processing architecture, but because of considerations such as reliability, survivability, and communication bandwidth, distributed processing architectures are often the only alternative. The distributed fusion problem is m...

Multisensor Information Fusion State Estimator for Systems with Random Sensor Errors

In this paper, a multisensor distributed information fusion state estimator for discrete time stochastic linear systems with random sensor errors is presented. Based on state-space model, the white noise estimator and the observation predictor are applied in this algorithm. Modern time series analysis method and Gevers-Wouters(G-W) algorithm are also used in this paper. The algorithm can deal w...

Deconvolution Two The Estimator