Reduced-complexity spatial and temporal processing of underwater acoustic communication signals

Abstract

Multichannel processing of high-speed underwater acoustic communication signals requires computationally intensive receiver algorithms. The size of adaptive filters, determined by the extent of ocean multipath, increases with signaling rate and limits system performance through large noise enhancement and increased sensitivity of computationally efficient algorithms to numerical errors. To overcome these limitations, reduction in receiver complexity is achieved by exploiting the relationship between optimal diversity combining and beamforming. Under relatively simple conditions, two adaptive receivers, one based on diversity combining which does not rely on any spatial signal distribution, and the other based on optimal beamforming, are shown to achieve the same performance. The beamforming approach, however, leads to a receiver of lower complexity. Carrying these observations over to a general case of broadband transmission through an unknown channel, a fully adaptive receiver is developed which incorporates a multi-input multi-output, many-to-few combiner, and a reduced-complexity multichannel equalizer. Receiver operations are optimized jointly to ensure minimum mean-squared error performance of data detection. Results of processing experimental shallow water data demonstrate the capability to fully exploit spatial diversity of underwater multipath while keeping the complexity at an acceptable level.