A signal enhancement algorithm is developed which seeks to recover a signal from noise-contaminated distorted measurements made on that signal. This ob,ject is achieved bj utilizing a set of properties which the signal is known o r is hypothesized as possessing. The measured signal is modified to the smallest degree necessary so as to sequentially possess each of the individual properties. Conditions for the algorithm's convergence are established in which the primary requirement is that a composite property mapping be closed. This is a relatively unrestrictive condition in comparison to that required of most existing signal enhancement algorithms. One of the more interesting and important uses of signal enhancement is concerned with applications in which the signal being enhanced is represented in a data or correlation matrix format. I n such applications, the eigencharacterization and structure (e.&, Toeplitz) of the matrix often provides useful signal properties. Several important property mappings for finding a matrix that possesses a specified linear structure or eigencharacterization and which lies closest to a given matrix are developed. It is shown that each of these property mappings is closed and can therefore be used as candidate property mappings in the proposed signal enhancement algorithm.

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