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Volume 31 Issue 3
Dec.  2010
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Deng Zi-li, Gu Lei, Ran Chen-jian. Measurement Fusion Steady-State Kalman Filtering Algorithm with Correlated Noises and Global Optimdity[J]. Journal of Electronics & Information Technology, 2009, 31(3): 556-560. doi: 10.3724/SP.J.1146.2007.01530
Citation: Deng Zi-li, Gu Lei, Ran Chen-jian. Measurement Fusion Steady-State Kalman Filtering Algorithm with Correlated Noises and Global Optimdity[J]. Journal of Electronics & Information Technology, 2009, 31(3): 556-560. doi: 10.3724/SP.J.1146.2007.01530

Measurement Fusion Steady-State Kalman Filtering Algorithm with Correlated Noises and Global Optimdity

doi: 10.3724/SP.J.1146.2007.01530
  • Received Date: 2007-12-19
  • Rev Recd Date: 2008-04-14
  • Publish Date: 2009-03-19
  • 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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