Probability, random processes, and estimation theory for engineers
Probability, random processes, and estimation theory for engineers
FIR filters and recursive forms for discrete-time state-space models
Automatica (Journal of IFAC)
Asymptotic noise gain of polynomial predictors
Signal Processing
Extrapolation, Interpolation, and Smoothing of Stationary Time Series
Extrapolation, Interpolation, and Smoothing of Stationary Time Series
A new FIR filter for state estimation and its application
Journal of Computer Science and Technology
Optimal horizons for a one-parameter family of unbiased FIR filters
Digital Signal Processing
Explicit Formula for Predictive FIR Filters and Differentiators Using Hahn Orthogonal Polynomials
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Brief A receding horizon unbiased FIR filter for discrete-time state space models
Automatica (Journal of IFAC)
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
Optimal and unbiased FIR estimates of clock state
SIP'10 Proceedings of the 9th WSEAS international conference on Signal processing
Studies of the noise power gain as a measure of errors for discrete-time transversal estimators
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
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
Discrete p-lag FIR smoothing of polynomial state-space models with applications to clock errors
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
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We address an unbiased finite impulse response (FIR) filter for discrete-time state-space models with polynomial representation of the states. The unique l-degree polynomial FIR filter gain and the estimate variance are found for a general case. The noise power gain (NG) is derived for white Gaussian noises in the model and in the measurement. The filter does not involve any knowledge about noise in the algorithm. It is unstable at short horizons, 2 ≤ N ≤ l, and inefficient (NG exceeds unity) in the narrow range l N ≤ Nb, where Nb is ascertained by the cross-components in the measurement matrix C. With N ≫ Nb, the filter NG poorly depends on C and fits the asymptotic function (l + 1)2/N. With very large N ⋙ 1, the estimate noise becomes negligible and the filter thus optimal in the sense of zero bias and zero noise. Having such properties, the proposed unbiased FIR filter fits well slowly changing with time models. An example is given for a two-state system.