This work presents a computational framework for the analysis and design of large-scale algorithms utilized in the estimation of acoustic, doubly-dispersive, randomly time-variant, underwater communication channels. Channel estimation results are used, in turn, in the proposed framework for the development of efficient high performance algorithms, based on fast Fourier transformations, for the search, detection, estimation and tracking (SDET) of underwater moving objects through acoustic wavefront signal analysis techniques associated with real-time electronic surveillance and acoustic monitoring (eSAM) operations. Particular importance is given in this work to the estimation of the range and speed of deep underwater moving objects modeled as point targets. The work demonstrates how to use Kronecker products signal algebra (KSA), a branch of finite-dimensional tensor signal algebra, as a mathematical language for the formulation of novel variants of parallel orthogonal matching pursuit (POMP) algorithms, as well as a programming aid for mapping these algorithms to large-scale computational structures, using a modified Kuck's paradigm for parallel computation.

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