Signal estimation with binary-valued sensors |
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Authors: | Leyi Wang Gang George Yin Chanying Li Weixing Zheng |
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Institution: | 1.Department of Electrical and Computer Engineering,Wayne State University,Detroit,USA;2.Department of Mathematics,Wayne State University,Detroit,USA;3.Department of Mechanical Engineering,The University of Hong Kong,Hong Kong,China;4.School of Computing and Mathematics,University of Western Sydney,Western Sydney,Australia |
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Abstract: | This paper introduces several algorithms for signal estimation using binary-valued output sensing. The main idea is derived
from the empirical measure approach for quantized identification, which has been shown to be convergent and asymptotically
efficient when the unknown parameters are constants. Signal estimation under binary-valued observations must take into consideration
of time varying variables. Typical empirical measure based algorithms are modified with exponential weighting and threshold
adaptation to accommodate time-varying natures of the signals. Without any information on signal generators, the authors establish
estimation algorithms, interaction between noise reduction by averaging and signal tracking, convergence rates, and asymptotic
efficiency. A threshold adaptation algorithm is introduced. Its convergence and convergence rates are analyzed by using the
ODE method for stochastic approximation problems. |
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Keywords: | |
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