Longwall mining automation an application of minimum-variance smoothing [Applications of Control]

@article{Einicke2008LongwallMA,
  title={Longwall mining automation an application of minimum-variance smoothing [Applications of Control]},
  author={Garry A. Einicke and Johnathon C. Ralston and Chad O. Hargrave and D. Reid and David W. Hainsworth},
  journal={IEEE Control Systems},
  year={2008},
  volume={28}
}
This article reviews the development of the minimum-variance smoother and describes its use in longwall automation. We describe both continuous- and discrete-time smoother solutions. It is shown, under suitable assumptions, that the two-norm of the smoother estimation error is less than that for the Kalman filter. A simulation study is presented to compare the performance of the minimum-variance smoother with the methods of H.E. Rauch et al. (1965), and D.C. Fraser and J.E. Potter (1969). 
View on IEEE
doi.org
47 Citations
Background Citations
15
Methods Citations
4

Figures from this paper

47 Citations

Citation Type

Citation Type

Iterative Smoother-Based Variance Estimation
The minimum-variance smoother solution for input estimation is described and it is shown that the resulting estimates are unbiased. The smoothed input and state estimates are used to iteratively…
A Solution to the Continuous-Time H ∞ Fixed-Interval Smoother Problem
—The minimum-variance fixed-interval smoother is a state-space realization of the Wiener solution generalized for time-varying problems. It involves forward and adjoint Wiener-Hopf factor inverses in…
A Solution to the Continuous-Time ${\rm H}_{\infty}$ Fixed-Interval Smoother Problem
  • G. Einicke
  • Mathematics
    IEEE Transactions on Automatic Control
  • 2009
TLDR
It is shown that the smoother exhibits an increase in mean-square-error, the error is bounded, and the upper error bound is greater than that for the H∞ filter.
The use of energy constraints within filtering and smoothing
  • G. Einicke
  • Mathematics
    IEEE Conference on Decision and Control and European Control Conference
  • 2011
TLDR
A low-cost filtering procedure is proposed in which the input measurements are constrained by nonlinear censuring functions and the resulting estimates are unbiased, and the estimation errors are bounded.
Dynamic Precise Positioning Method of Shearer Based on Closing Path Optimal Estimation Model
TLDR
A closing path optimal estimation model was proposed based on the motion characteristics of the shearer and a Kalman filter (KF) and it was verified that the model improved the positioning accuracy of theShearer by approximately an order of 1.
Bounded Constrained Filtering for GPS/INS Integration
TLDR
This paper considers estimation problems where inequality constraints are imposed on the outputs of linear systems and can be modeled by nonlinear functions, and establishes that the filter and smoother output estimates are unbiased, provided that the underlying probability density functions are even and the censoring functions are odd.
The application of frequency-weighting to improve filtering and smoothing performance
  • G. Einicke
  • Engineering
    2014 8th International Conference on Signal Processing and Communication Systems (ICSPCS)
  • 2014
Pioneering research on the perception of sounds at different frequencies was conducted by Fletcher and Munson in the 1930s. Their work led to a standard way of weighting measured sound levels within…
Iterative filtering and smoothing of measurements possessing poisson noise
  • G. Einicke
  • Mathematics
    IEEE Transactions on Aerospace and Electronic Systems
  • 2015
The minimum-variance filter and smoother are generalized to include Poisson-distributed measurement noise components. It is shown that the resulting filtered and smoothed estimates are unbiased. The…
A LiDAR-Aided Inertial Positioning Approach for a Longwall Shearer in Underground Coal Mining
TLDR
A Kalman filter (KF) model based on the observation provided by detecting hydraulic supports which are part of the longwall face, using the LiDAR, is established and the shearer positioning accuracy obtained using the proposed approach is higher.
An effective method for longwall automation in thin coal seam
TLDR
Remote integration control of the shearer machine is introduced, and an easy and achievable method called replicated trajectory cutting (RTC) is put forward to realize the automation of the mining machine to improve communication stability in the thin coal seam.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 11 REFERENCES
Optimal and robust noncausal filter formulations
  • G. Einicke
  • Mathematics
    IEEE Transactions on Signal Processing
  • 2006
TLDR
The paper describes an optimal minimum-variance noncausal filter or fixed-interval smoother that involves a cascade of a KalMan predictor and an adjoint Kalman predictor and demonstrates that optimal, robust, and extended Kalman smoothers can provide performance benefits.
An innovations approach to least-squares estimation--Part II: Linear smoothing in additive white noise
The innovations approach to linear least-squares approximation problems is first to "whiten" the observed data by a causal and invertible operation, and then to treat the resulting simpler…
Asymptotic Optimality of the Minimum-Variance Fixed-Interval Smoother
  • G. Einicke
  • Mathematics
    IEEE Transactions on Signal Processing
  • 2007
This correspondence investigates the asymptotic performance of the discrete-time and continuous-time, time-varying, minimum-variance, fixed-interval smoothers. Comparison theorems are generalized to…
Optimal Filtering
TLDR
This book helps to fill the void in the market and does that in a superb manner by covering the standard topics such as Kalman filtering, innovations processes, smoothing, and adaptive and nonlinear estimation.
The optimum linear smoother as a combination of two optimum linear filters
A solution to the optimum linear smoothing problem is presented in which the smoother is interpreted as a combination of two optimum linear filters. This result is obtained from the well-known…
A New Approach to Linear Filtering and Prediction Problems
The clssical filleting and prediclion problem is re-examined using the Bode-Shannon representation of random processes and the ?stat-tran-sition? method of analysis of dynamic systems. New result…
A schur method for solving algebraic Riccati equations
  • A. Laub
  • Mathematics, Computer Science
    1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes
  • 1978
TLDR
A new algorithm for solving algebraic Riccati equations (both continuous-time and discrete-time versions) is presented, a variant of the classical eigenvector approach and uses instead an appropriate set of Schur vectors thereby gaining substantial numerical advantages.
Two filter smoothing formulae by diagonalization of the Hamiltonian equations
We present a new approach to two filter smoothing formulae via diagonalization of the general time variant hamiltonian equations of the linear estimation problem. This approach shows the special role…
On the maximum likelihood estimates for linear dynamic systems
A steel slab or bloom containing 0.3 DIFFERENCE 0.15% by weight of C, 0.05 DIFFERENCE 0.60% by weight of Si, 0.60 DIFFERENCE 2.5% by weight of Mn, 0.010 DIFFERENCE 0.15% by weight of Nb, 0.005…
Asymptotic optimality of the minimumvariance fixedinterval smoother
  • IEEE Trans . Signal Process .
  • 2006
...
1
2
...