<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The generalized maximum likelihood algorithm is introduced for detecting the abrupt change in the band center of a fast-fluctuating Gaussian random process with the uniform spectral density. This algorithm has a simpler structure than the ones obtained by means of common approaches and it can be effectively implemented on the base of both modern digital signal processors and field-programmable gate arrays. By applying the multiplicative and additive local Markov approximation of the decision statistics and its increments, the analytical expressions are calculated for the false alarm and missing probabilities. And with the help of statistical simulation it is confirmed that the proposed detector is operable, while the theoretical formulas describing its quality and efficiency approximate satisfactorily the corresponding experimental data in a wide range of parameters of the observable data realization.</p></abstract>

The generalized maximum likelihood algorithm is introduced for detecting the abrupt change in the band center of a fast-fluctuating Gaussian random process with the uniform spectral density. This algorithm has a simpler structure than the ones obtained by means of common approaches and it can be effectively implemented on the base of both modern digital signal processors and field-programmable gate arrays. By applying the multiplicative and additive local Markov approximation of the decision statistics and its increments, the analytical expressions are calculated for the false alarm and missing probabilities. And with the help of statistical simulation it is confirmed that the proposed detector is operable, while the theoretical formulas describing its quality and efficiency approximate satisfactorily the corresponding experimental data in a wide range of parameters of the observable data realization.

Detecting an Unknown Abrupt Change in the Band Center of the Fast-Fluctuating Gaussian Random Process

International Laboratory of Statistics of Stochastic Processes and Quantitative Finance

Department of Mathematics, Physics and System Analysis, Faculty of Engineering

Department of Electronics and Nanoelectronics, Faculty of Electrical Engineering

Department of Probability Theory, Faculty of Mechanics and Mathematics

Measurement Science Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The generalized maximum likelihood algorithm is introduced for detecting the abrupt change in the band center of a fast-fluctuating Gaussian random process...

Detecting an Unknown Abrupt Change in the Band Center of the Fast-Fluctuating Gaussian Random Process

Publicado en línea: 24 ago 2020

Páginas: 187 - 195

Recibido: 04 jun 2020

Aceptado: 10 ago 2020

DOI: https://doi.org/10.2478/msr-2020-0023

Palabras clave
Fast-fluctuating random process, abrupt change, maximum likelihood method, discontinuous parameter, unknown band center, local Markov approximation method, false alarm probability, missing probability, statistical simulation

© 2020 Oleg Chernoyarov et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Detecting an Unknown Abrupt Change in the Band Center of the Fast-Fluctuating Gaussian Random Process

Publicado en línea: 24 ago 2020

Páginas: 187 - 195

Recibido: 04 jun 2020

Aceptado: 10 ago 2020

DOI: https://doi.org/10.2478/msr-2020-0023

Palabras claveFast-fluctuating random process, abrupt change, maximum likelihood method, discontinuous parameter, unknown band center, local Markov approximation method, false alarm probability, missing probability, statistical simulation

© 2020 Oleg Chernoyarov et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Palabras clave
Fast-fluctuating random process, abrupt change, maximum likelihood method, discontinuous parameter, unknown band center, local Markov approximation method, false alarm probability, missing probability, statistical simulation