Classification of non-stationary random signals using multiple hypotheses testing

We present a new non-stationary signal classification algorithm based on a time-frequency distribution and multiple hypothesis testing. The time-frequency distribution is used to construct a time-dependent quadratic discriminant function. At selected points in time we evaluate the discriminant function and form a set of statistics which are used to test multiple hypotheses. The multiple hypotheses are treated simultaneously using the generalised sequentially rejective Boferroni test to control the probability of incorrect classification of one class. Finally, we show the results of classifying time-dependent AR(1) processes which have identical expected instantaneous power and power spectra densities but different time-frequency representations.