Estimation Theory with Applications to Communication and Control

Sage, Andrew P.; Melsa, James L.; Steinway, W. J.

doi:10.1109/tsmc.1971.4308330

Cited by 271 publications

(128 citation statements)

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“…Let the auto-covariance function of the state vector x(k) be given by (3) in the semi-degenerate kernel form. Then the algorithms for the RLS Wiener FIR prediction and filtering estimates consist of (11)- (17). m-step ahead RLS Wiener FIR prediction estimate of the signal…”

Section: Rls Wiener Fir Prediction and Filtering Algorithmsmentioning

confidence: 99%

“…The RLS Wiener estimators do not bring unnecessary estimation errors caused by the approximations of the input noise variance and the input matrix. In the estimation problems, the filtering, smoothing and prediction algorithms are interesting [17]. Compared with the RLS Wiener estimators, by using updated observed values, the RLS Wiener FIR estimators calculate the estimates of the signal recursively in terms of observed values in the finite interval.…”

Section: Introductionmentioning

confidence: 99%

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RLS Wiener FIR Predictor and Filter Based on Innovation Approach in Linear Discrete-Time Stochastic Systems

Nakamori¹

2017

FSP

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This paper designs the recursive least-squares (RLS) Wiener finite impulse response (FIR) predictor and filter, based on the innovation approach, in linear discrete-time stochastic systems. It is assumed that the signal is observed with additive white noise and the signal process is uncorrelated with the observation noise process. This paper also presents the recursive algorithms for the estimation error variance functions of the proposed RLS Wiener FIR predictor and filter. A numerical simulation example shows the estimation characteristics of the RLS Wiener FIR predictor and filter. Specifically, the estimation characteristics of the proposed RLS Wiener FIR filter and predictor are compared with those of the existing RLS Wiener FIR filter and the RLS Wiener predictor, derived based on the existing RLS Wiener filter, respectively.

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Section: Rls Wiener Fir Prediction and Filtering Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RLS Wiener FIR Predictor and Filter Based on Innovation Approach in Linear Discrete-Time Stochastic Systems

Nakamori¹

2017

FSP

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“…Submission process being evaluated in the form of (2) is widely used in optimal filtration theory [7][8][9]. …”

Section: Development Of Channel Coefficients Model and Filteringmentioning

confidence: 99%

“…For optimal criterion of minimum mean squared error channel coefficients estimates used the method of Markov filtering of discrete random sequences [7][8][9], using developed dynamic model of channel coefficients in the form of a stochastic difference equation (2 …”

Section: Development Of Channel Coefficients Model and Filteringmentioning

confidence: 99%

Efficiency Estimation of MIMO Communication System Channel Coefficients Based on Markov Model

Parshin

Kudryashov

2016

MATEC Web Conf.

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Abstract. Based on Markov model of MIMO data transmission system channel coefficients from moving object held synthesis and analysis of channel coefficients error estimation. The model takes into account distribution configuration of reflectors in area, reception center and moving object location at a given time, kind of trajectory and speed of the moving object. A theory discrete Markov processes linear filtration is used by the development of estimation algorithm. Posteriori dispersions matrix is calculated. The simulation of filtering algorithm for different signal-interference situations is carried out.

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“…The adaptation of the E-step to the problem described above is achieved by an extended Kalman filter and smoother (EKFS) [5], described in Section II. However, the adaptation of the M-step in Section III is a contribution of this paper.…”

Section: Introductionmentioning

confidence: 99%

Maximum likelihood estimation of sensor and action model functions on a mobile robot

Stronger

Stone

2008

2008 IEEE International Conference on Robotics and Automation

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Abstract-In order for a mobile robot to accurately interpret its sensations and predict the effects of its actions, it must have accurate models of its sensors and actuators. These models are typically tuned manually, a brittle and laborious process. Autonomous model learning is a promising alternative to manual calibration, but previous work has assumed the presence of an accurate action or sensor model in order to train the other model. This paper presents an adaptation of the Expectation-Maximization (EM) algorithm to enable a mobile robot to learn both its action and sensor model functions, starting without an accurate version of either. The resulting algorithm is validated experimentally both on a Sony Aibo ERS-7 robot and in simulation.

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Estimation Theory with Applications to Communication and Control

Cited by 271 publications

References 0 publications

RLS Wiener FIR Predictor and Filter Based on Innovation Approach in Linear Discrete-Time Stochastic Systems

RLS Wiener FIR Predictor and Filter Based on Innovation Approach in Linear Discrete-Time Stochastic Systems

Efficiency Estimation of MIMO Communication System Channel Coefficients Based on Markov Model

Maximum likelihood estimation of sensor and action model functions on a mobile robot

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