Self-tuning Measurement Fusion White Noise Deconvolution Estimator
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 and unknown noise statistics, an on-line noise statistics estimator is presented by using the correlation method. Based on the self-tuning Riccati equation, a self-tuning weighted measurement fusion white noise deconvolution estimator is presented using the Kalman filtering method. It is proved that the self-tuning fusion white noise deconovlution estimator converges to the optimal fusion steady-state white noise deconvolution estimator in a realization by the dynamic error system analysis (DESA) method, so that it has the asymptotic global optimality. A simulation example for a tracking system with 3 sensors shows its effectiveness.
Multisensor Information Fusion Weighted Measurement Fusion Self-tuning Fuser White Noise Deconvolution Asymptotic Global Optimality Kalman Filtering Convergence
Xiaojun Sun Zili 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
1127-1132
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）