Statistical results for system identification based on quantized observations

Authors:
Fredrik Gustafsson;Rickard Karlsson
Affiliations:
Department of Elec. Eng., Linköping University, 58381 Linkoping, Sweden;NIRA Dynamics AB, Sweden
Venue:
Automatica (Journal of IFAC)
Year:
2009

Citing 6
Cited 5

System identification: theory for the user

System identification: theory for the user
An ultra-low energy asynchronous processor for Wireless Sensor Networks

ASYNC '06 Proceedings of the 12th IEEE International Symposium on Asynchronous Circuits and Systems
Asymptotically efficient parameter estimation using quantized output observations

Automatica (Journal of IFAC)
Quantization Noise: Roundoff Error in Digital Computation, Signal Processing, Control, and Communications

Quantization Noise: Roundoff Error in Digital Computation, Signal Processing, Control, and Communications
Effects of sampling and quantization on single-tone frequencyestimation

IEEE Transactions on Signal Processing
A theory of nonsubtractive dither

IEEE Transactions on Signal Processing

On identification of FIR systems having quantized output data

Automatica (Journal of IFAC)
Convergence analysis of an online approach to parameter estimation problems based on binary observations

Automatica (Journal of IFAC)
Generating dithering noise for maximum likelihood estimation from quantized data

Automatica (Journal of IFAC)
A new adaptive decentralized soft decision combining rule for distributed sensor systems with data fusion

Information Sciences: an International Journal
Robust distributed maximum likelihood estimation with dependent quantized data

Automatica (Journal of IFAC)

Quantified Score

Hi-index	22.15

Visualization

Abstract

System identification based on quantized observations requires either approximations of the quantization noise, leading to suboptimal algorithms, or dedicated algorithms tailored to the quantization noise properties. This contribution studies fundamental issues in estimation that relate directly to the core methods in system identification. As a first contribution, results from statistical quantization theory are surveyed and applied to both moment calculations (mean, variance etc) and the likelihood function of the measured signal. In particular, the role of adding dithering noise at the sensor is studied. The overall message is that tailored dithering noise can considerably simplify the derivation of optimal estimators. The price for this is a decreased signal to noise ratio, and a second contribution is a detailed study of these effects in terms of the Cramer-Rao lower bound. The common additive uniform noise approximation of quantization is discussed, compared, and interpreted in light of the suggested approaches.