Convergence analysis of an online approach to parameter estimation problems based on binary observations

Authors:
Kian Jafari;JéRôMe Juillard;Morgan Roger
Affiliations:
-;-;-
Venue:
Automatica (Journal of IFAC)
Year:
2012

Citing 7
Cited 0

Adaptive filter theory (2nd ed.)

Adaptive filter theory (2nd ed.)
Identification of Wiener systems with binary-valued output observations

Automatica (Journal of IFAC)
Towards identification of Wiener systems with the least amount of a priori information on the nonlinearity

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

Automatica (Journal of IFAC)
Adaptive filtering using quantized output measurements

IEEE Transactions on Signal Processing
Linear systems identification from random threshold binary data

IEEE Transactions on Signal Processing
Joint identification of plant rational models and noise distribution functions using binary-valued observations

Automatica (Journal of IFAC)

Quantified Score

Hi-index	22.14

Visualization

Abstract

In this paper, we present an online identification method to the problem of parameter estimation from binary observations. A recursive identification algorithm with low-storage requirements and computational complexity is derived. We prove the convergence of this method provided that the input signal satisfies a strong mixing property. Some simulation results are then given in order to illustrate the properties of this method under various scenarios. This method is appealing in the context of micro-electronic devices since it only requires a 1-bit analog-to-digital converter, with low power consumption and minimal silicon area.