Underwater communication with vertical receiver arrays

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This chapter discusses space-time adaptive processing in the context of blind equalisation of underwater acoustic channels. To justify the great efforts in signal processing necessary to establish a reliable underwater communication link, special emphasis has been put on the characterisation of the transmission medium. The typical underwater acoustic channel has been found to be overspread due to rapid temporal and spectral variations, therefore calling for special signal processing techniques. Their development with time has been summarised in a brief history of underwater acoustic communication, highlighting only key contributions. Subsequently, a spatial-temporal receiver architecture has been introduced that efficiently allows for joint processing of signals received on many sensors. Based on this structure, a signal model suitable for the description of blind space-time adaptive equalisation algorithms has been developed. Then, the well known constant modulus algorithm for blind channel equalisation has been treated as an example for the class of stochastic gradient descent algorithms. The results obtained with measured shallow water communication data have demonstrated the general applicability of this algorithm but have also shown its slow convergence as a major drawback. As an alternative, an adaptive multichannel version of the Shalvi-Weinstein algorithm for blind equalisation has been derived. This algorithm is closely related to the constant modulus algorithm but shows a better convergence behaviour. This theoretically obtained result has been exemplarily verified by analysing the same data set as with the CMA. Although in this chapter we have presented two successful strategies for blind space-time adaptive equalisation with applications to underwater acoustic communication, blind equalisation is still an active area of research. In particular, for many proposed algorithms the bridge between theory and application is still lacking, constituting a rich set of research challenges for many years to come.