Abrupt change of the mathematical expectation of the random process with unknown intensity

Chernoyarov, Oleg V.; Salnikova, Alexandra V.; Rozhkova, D.S.

doi:10.12988/ams.2015.510630

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…Other designations are the following: 13 is the retriever of the location of the input signal greatest maximum (extremator) within the interval   In order to determine the efficiency of the synthesized detection (7) and estimation (9) algorithms, we found the asymptotically exact (with increasing min  (4)) expressions for their characteristics such as type I and II error probabilities (for algorithm (7)) and conditional biases and variances of estimates (for algorithm (9)), using a local Markov approximation method [8,9]. We also carried out experimental tests for the performance of the detector (7) and the measurer (9) by methods of statistical computer simulation.…”

Section: Figure I Block Diagram Of the Detector/measurer Of The Abrumentioning

confidence: 99%

Detection and Measurement of the Abrupt Change of the Power Parameters of the Fast-Fluctuating Gaussian Random Process

Chernoyarov

Shahmoradian

Salnikova

et al. 2017

Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)

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Abstract-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.

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Section: Figure I Block Diagram Of the Detector/measurer Of The Abrumentioning

confidence: 99%

Detection and Measurement of the Abrupt Change of the Power Parameters of the Fast-Fluctuating Gaussian Random Process

Chernoyarov

Shahmoradian

Salnikova

et al. 2017

Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)

View full text Add to dashboard Cite

show abstract

Abrupt change of the mathematical expectation of the random process with unknown intensity

Cited by 1 publication

References 6 publications

Detection and Measurement of the Abrupt Change of the Power Parameters of the Fast-Fluctuating Gaussian Random Process

Detection and Measurement of the Abrupt Change of the Power Parameters of the Fast-Fluctuating Gaussian Random Process

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