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Measurement Fusion Steady-State Kalman Filtering Algorithm with Correlated Noises and Global Optimdity |
Deng Zi-li;Gu Lei; Ran Chen-jian |
Department of Automation, Heilongjiang University, Harbin 150080, China |
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Abstract For the multisensor linear discrete time-invariant stochastic control systems with correlated input and measurement white noises, and with correlated measurement white muses, a weighted measurement fusion steady-state Kalman filtering algorithm is presented by using the Weighted Least Squares(WLS)method. It can handle the fused filtering , smoothing and prediction problems for the state, white noise and signal. Based on the steady-state information filter, it is proved that it is completely functionally equivalent to the centralized measurement fusion steady-state Kalman filtering algorithm, so that it has asymptotic global optimality, and can reduced the computational burden. A simulation examples for tracking systems verifies its functional equivalence.
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Received: 19 December 2007
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