White Noise Estimation Theory Based on Kalman Filtering
Deng Zi · 2003
By using the Kalman filtering method, a unified and general white noise estimation theory is presented for the first time. It can handle the filtering, smoothing and prediction problems in a unified framework for both the input white noise and measurement white noise in linear discrete time-varying and time-invariant stochastic systems. The optimal and steady-state white noise estimators are presented, and white noise innovation filters and Wiener filters are also presented. They can be applied to seismic data processing in oil exploration, and provide a new tool to solve the state and signal estimation problems. Two simulation examples show their effectiveness.