Physically constrained maximum-likelihood mode filtering and its application as a pre-processing method for underwater acoustic communication

Mode filtering is most commonly implemented using the sampled mode shapes or pseudo-inverse algorithms. [Buck et al. (1998)] placed these techniques in the context of a broader maximum a posteriori (MAP) framework. However, the MAP algorithm requires that the signal and noise statistics be known a priori. Adaptive array processing algorithms are candidates for improving performance without the need for a priori signal and noise statistics. A variant of the physically constrained, maximum-likelihood (PCML) algorithm [Kraay and Baggeroer (2007)] is developed for mode filtering that achieves the same performance as the MAP mode filter yet does not need a priori knowledge of the signal and noise statistics. The central innovation of this adaptive mode filter is that the received signal’s sample covariance matrix, as estimated by the algorithm, is constrained to be that which can be physically realized given a modal propagation model and an appropriate noise model. Simulation and data processing results from the Shallow Water 2006 experiment are presented along with the application of the mode filter as a broadband pre-processor for multichannel underwater acoustic communication systems. [This work was supported by ONR through ONR Grant Nos. N00014-05-10085 and N00014-06-10788.]