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Advanced signal processing Dr. Mohamad KAHLIL Islamic University of Lebanon. Outline. Random variables Histogram, Mean, Variances, Moments, Correlation, types, multiple random variables Random functions Correlation, stationarity, spectral density estimation methods
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Advanced signal processing Dr. Mohamad KAHLIL Islamic University of Lebanon
Outline • Random variables • Histogram, Mean, Variances, Moments, Correlation, types, multiple random variables • Random functions • Correlation, stationarity, spectral density estimation methods • Signal modeling: AR, MA, ARMA, • Detection and classification in signals • Advanced applications on signal processing: • Time frequency and wavelet
Chapter 4: detection and classification in random signals • Detection • Definition • Statistical tests for detection • Likelihood ratio • Example of detection when change in mean • Example of detection when change in variances • Multidimensional detection
Detection: definition Hypotheses : estimated Known or unknown
Gaussian distributions Normal distributions
Chi2 distributions Loi du Chi 2 (Khi-two of Pearson) 10 dof 15 dof chi2 with k degree of freedom E[chi2]=k Variance of Chi2=2k
Fisher Test Student distribution F(6,7) F(6,10) Student with k degree of freedom Fisher-Snédécor Distribution Fisher with k and l degree of freedom Example: Detection in signals
Detection: definition Hypotheses : estimated Known or unknown
Parameters definition • False alarm • Detect H1, H0 is correct • Detection • Detect H1, H1 is correct • Miss detection • Detect H0, H1 is correct
Likelihood ratio • Detection in signals
Variation in mean • Detection in mean H0: z(t) = 0 + b(t) = b(t) H1: z(t) = m + b(t)
Detection in variance • Detection in variance
Parameters • False Alarm probability • Detection probability
Neymen pearson method • Fix the probability of false alarm • Estimate the threshold
Detection: multidimensional case • Multidimensional case