On identification of FIR systems having quantized output data

Authors:
Boris I. Godoy;Graham C. Goodwin;Juan C. AgüEro;DamiáN Marelli;TorbjöRn Wigren
Affiliations:
Centre for Complex Dynamic Systems and Control, The University of Newcastle, Callaghan NSW 2308, Australia;Centre for Complex Dynamic Systems and Control, The University of Newcastle, Callaghan NSW 2308, Australia;Centre for Complex Dynamic Systems and Control, The University of Newcastle, Callaghan NSW 2308, Australia;Centre for Complex Dynamic Systems and Control, The University of Newcastle, Callaghan NSW 2308, Australia;Division of Systems and Control, Department of Information Technology, Uppsala University, Uppsala, Sweden
Venue:
Automatica (Journal of IFAC)
Year:
2011

Citing 11
Cited 5

Identifying MIMO Wiener systems using subspace model identification methods

Signal Processing - Special issue: subspace methods, part II: system identification
A view of the EM algorithm that justifies incremental, sparse, and other variants

Learning in graphical models
State-Space Models: From the EM Algorithm to a Gradient Approach

Neural Computation
Identification of Wiener systems with binary-valued output observations

Automatica (Journal of IFAC)
Statistical results for system identification based on quantized observations

Automatica (Journal of IFAC)
Inference in Hidden Markov Models

Inference in Hidden Markov Models
Recursive expectation-maximization (EM) algorithms for time-varyingparameters with applications to multiple target tracking

IEEE Transactions on Signal Processing
Adaptive filtering using quantized output measurements

IEEE Transactions on Signal Processing
Nonlinear filtering via generalized Edgeworth series andGauss-Hermite quadrature

IEEE Transactions on Signal Processing
Iterative receivers for space-time block-coded OFDM systems in dispersive fading channels

IEEE Transactions on Wireless Communications
Least squares quantization in PCM

IEEE Transactions on Information Theory

Input design in worst-case system identification with quantized measurements

Automatica (Journal of IFAC)
Identification of ARMA models using intermittent and quantized output observations

Automatica (Journal of IFAC)
Blind system identification using precise and quantized observations

Automatica (Journal of IFAC)
Recursive projection algorithm on FIR system identification with binary-valued observations

Automatica (Journal of IFAC)
Robust distributed maximum likelihood estimation with dependent quantized data

Automatica (Journal of IFAC)

Quantified Score

Hi-index	22.15

Visualization

Abstract

In this paper, we present a novel algorithm for estimating the parameters of a linear system when the observed output signal is quantized. This question has relevance to many areas including sensor networks and telecommunications. The algorithms described here have closed form solutions for the SISO case. However, for the MIMO case, a set of pre-computed scenarios is used to reduce the computational complexity of EM type algorithms that are typically deployed for this kind of problem. Comparisons are made with other algorithms that have been previously described in the literature as well as with the implementation of algorithms based on the Quasi-Newton method.