Steady-state Optimal Measurement Fusion White Noise Deconvolution Estimators
White noise deconvolution or input white noise estimation has a wide range of applications including oil seismic exploration, communication, signal processing, and state estimation. For the multisensor linear discrete time-invariant stochastic systems with correlated measurement noises, a steady-state measurement fusion system is obtained by the weighted least square (WLS) method. A steady-state optimal weighted measurement fusion white noise deconvolution estimator is presented using the Kalman filtering method. By a new derivation method, it is rigorously proved that the steady-state white noise deconvolution fuser is numerically identical to the centralized steady-state white noise deconvolution fuser, i.e. it has the asymptotically global optimality. It can reduce the computational burden because of the lower dimension of the measurement vector. A simulation example for the Bernoulli-Gaussian input white noise shows the effectiveness of the proposed results.
Multisensor Information Fusion Weighted Measurement Fusion White Noise Deconvolution Global Optimality Kalman Filtering
Xiao-Jun Sun Zi-Li Deng
Department of Automation, Heilongjiang University, Harbin 150080, China
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
2623-2628
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）