P.S. Kim, Y.K. Kim, and S.W. Kim (Korea)
State estimation; Maximum likelihood (ML); FIR filter;Unbiasedness property; Deadbeat property; Fault detection.
In this paper, a new maximum likelihood filter with fi nite impulse response (FIR) structures is proposed for state space models with both system and observation noises. This filter is called the maximum likelihood FIR (MLF) filter. The proposed MLF filter doesn’t require a priori in formation of the window initial state and processes the fi nite observations on the most recent window linearly. The proposed MLF filter is first represented in a batch form, and then in an iterative form for computational advantage. The proposed MLF filter has good inherent properties such as time-invariance, unbiasedness, deadbeat, and robustness. To show the useful application of the proposed MLF filter, the problem of a fault detection is considered on a DC mo tor system. Via simulations, it is shown that the MLF filter ing approach outperforms the Kalman filtering approach.
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