Processing of large sets of stochastic signals: filtering based on piecewise interpolation technique

Suppose $K_{{}_Y}$ and $K_{{}_X}$ are large sets of observed and reference signals, respectively, each containing $N$ signals. Is it possible to construct a filter $F$ that requires a priori information only on few signals, $p\ll N$, from $K_{{}_X}$ but performs better than the known filters based on a priori information on every reference signal from $K_{{}_X}$? It is shown that the positive answer is achievable under quite unrestrictive assumptions. The device behind the proposed method is based on a special extension of the piecewise linear interpolation technique to the case of random signal sets. The proposed technique provides a single filter to process any signal from the arbitrarily large signal set. The filter is determined in terms of pseudo-inverse matrices so that it always exists.