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Real-Time Smoothing Interval Estimation Using Correlation Functions in Random Processes

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This study explores the methodology for real-time smoothing interval estimation using correlation function techniques for random processes. The authors, Dmitry Bychkov and Mark Polyak from Saint Petersburg State University of Aerospace Instrumentation, present an analysis of the Z-coefficient as a measure of process oscillativity. Key findings include the relationship between the number of crossovers and extrema within specified time intervals, illustrated through various graphs demonstrating the Z-coefficient under different sampling frequencies (100Hz). The study offers practical insights and applications in telemetry and oscillatory process analysis.

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Real-Time Smoothing Interval Estimation Using Correlation Functions in Random Processes

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  1. Saint-Petersburg State University of Aerospace Instrumentation SMOOTHING INTERVAL ESTIMATION USING CORRELATION FUNCTION TYPE FOR A RANDOM PROCESS IN REAL TIME Dmitry Bychkov, Mark Polyak Saint-Petersburg, 2009

  2. Telemetry information

  3. Z-coefficient - measure of process oscillativity where: n(H,T) – number of crossovers of the H level in time interval T; nmax(T)– number of extremums in time interval T;

  4. Graph of Z- coefficient for two types (sampling frequency 100Hz).

  5. Graph of average values of Z- coefficient (sampling frequency 100Hz).

  6. RESULTS

  7. Graph of n as a function of Fc Fd = 50 Hz

  8. Fd = 100 Hz

  9. Static table (Example for the first type of random process with γ = 50 Hz) Fc α

  10. Practical example

  11. Thank you for your attention!

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