A crucial aspect of Time-Frequency (TF) analysis is the identification of separate components in a multi component signal. Wigner-Ville distribution is the classical tool for representing such signals but suffers from cross-terms. Other methods which are members of Cohen’s class distributions also aim to remove the cross terms by masking the Ambiguity Function (AF) but they result in reduced resolution. Most practical time-varying signals are in the form of weighted trajectories on the TF plane and many others are sparse in nature. Therefore the problem is cast as TF distribution reconstruction using a subset of AF domain coefficients and sparsity assumption in recent studies. Sparsity can be achieved by constraining or minimizing the norm. In this article, a Projections Onto Convex Sets (POCS) based minimization approach is proposed to obtain a high resolution, crossterm-free TF distribution for a given signal. The new method does not require any parameter adjustment to obtain a solution. Experimental results are presented.