  In this paper, a whitening-based algorithm has been applied to sparse
non-negative matrix factorization (NMF) as a preprocessing practice enhancing
the identification of buried object, significantly. In fact, without utilizing
a suitable whitening algorithm, the input signals are very similar to one
another. Therefore, conventional approaches are not able to identify and
properly discriminate desired buried objects. A suitable whitening algorithm
makes the covariance matrix diagonal, such that the processed signals highly
resembles the unprocessed versions. Such a similarity would be helpful to
identify new received signal. In this study, we have employed the zero-phase
component analysis (ZCA) as our whitening method. Hence, although signals of
different targets are very similar in many cases, the mentioned advantages,
achieved by whitening method, make identification of targets much easier.
