BROAD-BAND MODEL-BASED PROCESSING FOR SHALLOW OCEAN ENVIRONMENTS

Most acoustic sources found in the ocean environment are spatially com plex and broadband, In the case of shallow water propagation, these so urce characteristics complicate the analysis of received acoustic data considerably. A common approach to the broadband problem is to decomp ose the received signal into a set of narrow-band lines. This then all ows the problem to be treated as a multiplicity of narrow-band problem s. Here a model-based approach is developed for the processing of data received on a vertical array from a broadband source where it is assu med that the propagation is governed by the normal-mode model. The goa l of the processor is to provide an enhanced (filtered) version of the pressure at the array and the modal functions. Thus a pre-processor i s actually developed, since one could think of several applications fo r these enhanced quantities such as localization, modal estimation, et c. It is well-known that in normal-mode theory a different modal struc ture evolves for each temporal frequency; thus it is not surprising th at the model-based solution to this problem results in a scheme that r equires a ''bank'' of narrow-band model-based processors-each with its own underlying modal structure for the narrow frequency band it opera tes over. The ''optimal'' Bayesian solution to the broadband pressure field enhancement and modal function extraction problem is developed. It is shown how this broadband processor can be implemented (using a s uboptimal scheme) in pseudo real time due to its inherent parallel str ucture. A set of noisy broadband data is synthesized to demonstrate ho w to construct the processor and achieve a minimum variance (optimal B ayesian) design. It is shown that both broadband pressure-field and mo dal function estimates can be extracted illustrating the feasibility o f this approach. (C) 1998 Acoustical Society of America.