Detection and Measurement of the Abrupt Change of the Power Parameters of the Fast-Fluctuating Gaussian Random Process
- 10.2991/msam-17.2017.37How to use a DOI?
- random process; abrupt change; detection; estimation; maximum likelihood method; parametrical prior uncertainty; local Markov approximation method; statistical computer simulation
We introduce the technically simple approach to determining the abrupt change of the unknown mathematical expectation and dispersion of the low-frequency fast-fluctuating Gaussian random process against white noise. For this purpose, we determine new approximations of the decision statistics for various hypotheses, we carry out their maximization in terms of unknown parameters, and we develop the block diagrams for the corresponding detectors and measurers in the form of the comparatively simple single-channel units. For the analytical analysis of the performance of the synthesized algorithms, the asymptotically exact expressions for their characteristics, specifically – type I and type II error probabilities (when an abrupt change point is detected) and conditional biases and variances of the estimates (when measuring the parameters of the analyzed random process), are obtained by means of local Markov approximation method. The experimental testing of the presented theoretical results is implemented by the methods of statistical computer simulation.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Oleg V Chernoyarov AU - Mahdi M Shahmoradian AU - Alexandra V Salnikova AU - Ilya A Buravlev PY - 2017/03 DA - 2017/03 TI - Detection and Measurement of the Abrupt Change of the Power Parameters of the Fast-Fluctuating Gaussian Random Process BT - Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017) PB - Atlantis Press SP - 164 EP - 168 SN - 1951-6851 UR - https://doi.org/10.2991/msam-17.2017.37 DO - 10.2991/msam-17.2017.37 ID - Chernoyarov2017/03 ER -