Crossfrequency estimation for deconvolution

To reduce distortion, a technique is presented for deconvolving the effect of the distorting system on a desired signal. The technique does not require any prior assumptions about the distorting system other than that it is linear and time-invariant. However, we must assume that the desired signal is a sample function from a cyelostationary (CS) random process with known statistics. Many signals of practical importance can be modelled as CS including PAM, FSK, PWM, etc. The technique utilizes inverse filtering with an estimate of the distorting system function which is obtained exclusively from the observed signal and the known and computed properties of CS processes.