Interference term elimination of the discrete wigner distribution using nonlinear filtering

This paper introduces a new nonlinear filtering approach for the removal of cross terms in the discrete Wigner distribution. Realizing that linear smoothing kernels are unable to completely cancel the cross-terms without compromising time-frequency concentration and resolution of the auto-terms, a nonlinear filtering algorithm is devised where the filter automatically adapts to the rapidly changing nature of the Wigner distribution plane. Varying the filter behavior from an identity operation at one extreme to a low pass linear filter at the other, a near optimal removal of cross terms is achieved. Unlike traditional smoothing and optimal kernel design techniques, this algorithm does not reduce the time-frequency resolution and concentration of the auto-terms.