A method of extending towed array measurements that provides an aperture greater than that of the physical array is presented. Such a technique can be used by matched-field estimators to obtain information about the range and depth of a source and in other towed array applications requiring a very large aperture. The approach is to combine coherently the acoustic signals arriving at a moving array of hydrophones by making proper compensation through a factor that corrects for considerable fluctuations in phase irregularities in the tow path of the physical array as well as fluctuations in amplitude experienced during the coherent integration time. In this manner, the finite aperture of the physical array is exploited in a process that synthesizes the extended aperture of the method. The concept is based on an algorithm that we call an ‘‘overlap correlator,’’ which provides the phase correction factor by correlating overlapping space samples of the acoustic signal received at successive moments by the moving towed array. This is in contrast to the standard, passive synthetic aperture technique, which requires either highly accurate a priori knowledge of the source frequency or a maneuver in order to obtain a wavenumber or bearing estimate. The algorithm has been tested on numerical data generated by the SACLANT Undersea Research Centre’s normal mode model SNAP. The effects of space and time coherence of the signal and the random and systematic errors on the extended towed array measurements are examined and used to derive guidelines for experimental applications of this algorithm.

A model-based approach is proposed to solve the ocean acoustic signal processing problem that is based on a state-space representation of the normal-mode propagation model. It is shown that this representation can be utilized to spatially propagate both modal (depth) and range functions given the basic parameters (wave numbers, etc.) developed from the solution of the associated boundary value problem. This model is then generalized to the stochastic case where an approximate Gauss–Markov model evolves. The Gauss–Markov representation, in principle, allows the inclusion of stochastic phenomena such as noise and modeling errors in a consistent manner. Based on this framework, investigations are made of model-based solutions to the signal enhancement, detection and related parameter estimation problems. In particular, a modal/pressure field processor is designed that allows in situ recursive estimation of the sound velocity profile. Finally, it is shown that the associated residual or so-called innovation sequence that ensues from the recursive nature of this formulation can be employed to monitor the model’s fit to the data and also form the basis of a sequential detector.

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