Abstract
Up to this point, we have been treating binary-valued observations. The fundamental principles and basic algorithms for binary-valued observations can be modified to handle quantized observations as well. One way to understand the connection is to view a quantized observation as a vector-valued binary observation in which each vector component represents the output of one threshold, which is a binary-valued sensor. The dimension of the vector is the number of the thresholds in the quantized sensor.
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L.Y. Wang and G. Yin, Asymptotically efficient parameter estimation using quantized output observations, Automatica, 43 (2007), 1178–1191.
H. Abut, Ed., Vector Quantization, IEEE Press Selected Reprint Series, IEEE, Piscataway, 1990.
J.C. Aguero, G.C. Goodwin, J.I. Yuz, System identification using quantized data, in Proc. 46th IEEE Conf. Decision Control, 4263–4268, 2007.
C.S. Güntürk, One-bit sigma-delta quantization with exponential accuracy, Comm. Pure Appl. Math., 56 (2003), 1608–1630.
A. Gersho and R.M. Gray, Vector Quantization and Signal Compression, Kluwer, Norwell, 1992.
K. Sayood, Introduction to Data Compression, 2nd ed., Morgan Kaufmann, San Francisco, 2000.
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Wang, L.Y., Yin, G.G., Zhang, JF., Zhao, Y. (2010). Quantized Identification and Asymptotic Efficiency. In: System Identification with Quantized Observations. Systems & Control: Foundations & Applications. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4956-2_6
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DOI: https://doi.org/10.1007/978-0-8176-4956-2_6
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