Estimating the number of signals with unknown parameters under Gaussian noises

Model order selection is an important stage in many technical areas. Estimating the number of signals with unknown parameters is a special case of the model order selection problem. We describe a class of signal parameters for which we show that the widely used maximum likelihood method is useless f...

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Bibliographic Details
Published in2022 International Telecommunications Conference (ITC-Egypt) pp. 1 - 5
Main Authors Kharin, Aleksandr, Pergamenchtchikov, Serguei
Format Conference Proceeding
LanguageEnglish
Published IEEE 26.07.2022
Subjects
abridged error probability
Analytical models
Error probability
estimation problem for the number of signals
Gaussian noise
Maximum likelihood estimation
maximum likelihood method
model order selection
Numerical models
penalty terms
Performance analysis
signals in noise
Telecommunications
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Summary:Model order selection is an important stage in many technical areas. Estimating the number of signals with unknown parameters is a special case of the model order selection problem. We describe a class of signal parameters for which we show that the widely used maximum likelihood method is useless for estimating the number of signals. It is established, that the amplitude parameters belong to this class. Therefore, we study the estimation problem for the number of signals with unknown amplitudes in discrete time. Five algorithms for estimating the number of signals are described, including the new one. Finally, we provide a performance analysis of these algorithms, comparing them using both analytical and numerical approaches.
DOI:10.1109/ITC-Egypt55520.2022.9855685