Abstract
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.
Highlights
This work formulates a computational framework for the development of efficient algorithms to effect computational signal processing operations to address the problem of electronic surveillance and acoustic monitoring of deep underwater moving objects
The results presented here are based on two additional works: first, on the work of Carrascosa, P.C. and Stojanovic, M. on underwater acoustic channel estimation, where they introduce spatial multiplexing to increase the data rate supported by the band-limited restriction of the channel [9,10]; second, on the work of Yatawatta, S. and Petropulu, A. on blind channel estimation, where they allow the users at the transmitter to transmit simultaneously, without any bandwidth restrictions, each user utilizing a single antenna [11]
This paper presented a computational framework for the analysis and design of signal processing algorithms utilized in the estimation of acoustic, doubly-dispersive, randomly time-variant, deep underwater communication channels
Summary
This work introduces innovation along three main venues: (i) using a mathematical language formulation to describe the processes of channel estimation and multiple object tracking in a unified manner; (ii) developing unique cyclic discrete time-frequency distributions for range-Doppler estimation; and (iii) exploiting the algebraic properties of block circulant structures in order to reduce the overall computational complexity of channel estimation algorithms.
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