Estimating the number of signals with unknown parameters under Gaussian noises

• Published in: 2022 International Telecommunications Conference (ITC-Egypt) pp. 1 - 5
• Main Authors: Kharin, Aleksandr, Pergamenchtchikov, Serguei
• Format: Conference Proceeding
• Language: English
• 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
• DOI: 10.1109/ITC-Egypt55520.2022.9855685

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.