FIR filters and recursive forms for discrete-time state-space models
Automatica (Journal of IFAC)
A new FIR filter for state estimation and its application
Journal of Computer Science and Technology
Brief paper: Adaptive IIR/FIR fusion filter and its application to the INS/GPS integrated system
Automatica (Journal of IFAC)
Optimal horizons for a one-parameter family of unbiased FIR filters
Digital Signal Processing
Unbiased FIR filtering of discrete-time polynomial state-space models
IEEE Transactions on Signal Processing
A discrete-time sliding window observer for Markovian switching system: an LMI approach
Control and Intelligent Systems
An alternative FIR filter for state estimation in discrete-time systems
Digital Signal Processing
Fir smoothing of discrete-time polynomial signals in state space
IEEE Transactions on Signal Processing
Linear optimal FIR estimation of discrete time-invariant state-space models
IEEE Transactions on Signal Processing
A Kalman-like algorithm with no requirements for noise and initial conditions
NEHIPISIC'11 Proceeding of 10th WSEAS international conference on electronics, hardware, wireless and optical communications, and 10th WSEAS international conference on signal processing, robotics and automation, and 3rd WSEAS international conference on nanotechnology, and 2nd WSEAS international conference on Plasma-fusion-nuclear physics
Filtering of discrete-time state-space models with the p-shift Kalman-like unbiased FIR algorithm
TELE-INFO'11/MINO'11/SIP'11 Proceedings of the 10th WSEAS international conference on Telecommunications and informatics and microelectronics, nanoelectronics, optoelectronics, and WSEAS international conference on Signal processing
FIR filtering based image stabilization mechanism for mobile video appliances
CIS'04 Proceedings of the First international conference on Computational and Information Science
A new parity space approach to fault detection for general systems
HPCC'05 Proceedings of the First international conference on High Performance Computing and Communications
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This paper concerns with a new linear finite impulse response (FIR) filter called the receding horizon unbiased FIR (RHUF) filter for the state estimation in discrete-time state space models. To obtain the RHUF filter, linearity, unbiasedness and FIR structure will be required beforehand in addition to a performance criteria of minimum variance. The RHUF filter is obtained by directly solving an optimization problem with the unbiasedness constraint. The RHUF filter has time-invariance and deadbeat properties. The RHUF filter is represented in both a batch form and an iterative form. It is shown that the RHUF filter is equivalent to the existing receding horizon Kalman FIR (RHKF) filter whose optimality is not clear to understand. The former is more systematic and logical, while the latter is heuristic due to handling of infinite covariance of the initial state information.