Matched-field processing (MFP) localizes sources more accurately than plane-wave beamforming by employing full-wave acoustic propagation models for the cluttered ocean environment. The minimum variance distortionless response MFP (MVDR-MFP) algorithm incorporates the MVDR technique into the MFP algorithm to enhance beamforming performance. Such an adaptive MFP algorithm involves intensive computational and memory requirements due to its complex acoustic model and environmental adaptation. The real-time implementation of adaptive MFP algorithms for large surveillance areas presents a serious computational challenge where high-performance embedded computing and parallel processing may be required to meet real-time constraints. In this paper, three parallel algorithms based on domain decomposition techniques are presented for the MVDR-MFP algorithm on distributed array systems. The parallel performance factors in terms of execution times, communication times, parallel efficiencies, and memory capacities are examined on three potential distributed systems including two types of digital signal processor arrays and a cluster of personal computers. The performance results demonstrate that these parallel algorithms provide a feasible solution for real-time, scalable, and cost-effective adaptive beamforming on embedded, distributed array systems.

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In the highly cluttered undersea environment, sonar array systems require enhanced acoustic signal processing algorithms and sophisticated architectures in order to meet dependability and real-time mission requirements. The probability of hydrophone and processing element failures is very high in such severe operating environments. Adaptive matched-field processing (MFP) algorithms localize sources accurately with moderate levels of signal-to-noise ratio (SNR) and precise knowledge about environments by employing full-wave acoustic propagation models. However, they highly distort output beam patterns with significant increase of sidelobes in the presence of environmental mismatches and element failures. These problems make the development of advanced fault-tolerant signal processing algorithms imperative to tolerate the element failures in cases where replacement of defective elements is impossible or impractical. In this paper, three fault-tolerant MFP algorithms are presented to compensate for the performance degradation generated by the inherent failure characteristics of vertical line arrays. The beamforming performance and computational complexities for these fault-tolerant algorithms are analyzed in terms of the number of faulty elements, their positions in the array, and SNRs. The simulation results demonstrate that these fault-tolerant techniques provide a feasible solution for real-time and highly reliable beamforming implementation on sonar array systems.

Continuous innovations in adaptive matched-field processing (MFP) algorithms have presented significant increases in computational complexity and resource requirements that make development and use of advanced parallel processing techniques imperative. In real-time sonar systems operating in severe underwater environments, there is a high likelihood of some part of systems exhibiting defective behavior, resulting in loss of critical network, processor, and sensor elements, and degradation in beam power pattern. Such real-time sonar systems require high reliability to overcome these challenging problems. In this paper, efficient fault-tolerant parallel algorithms based on coarse-grained domain decomposition methods are developed in order to meet real-time and reliability requirements on distributed array systems in the presence of processor and sensor element failures. The performance of the fault-tolerant parallel algorithms is experimentally analyzed in terms of beamforming performance, computation time, speedup, and parallel efficiency on a distributed testbed. The performance results demonstrate that these fault-tolerant parallel algorithms can provide real-time, scalable, lightweight, and fault-tolerant implementations for adaptive MFP algorithms on distributed array systems.

Matched-field processing (MFP) localizes sources more accurately than plane-wave beamforming by employing full-wave acoustic propagation models for the cluttered ocean environment. The minimum variance distortionless response MFP (MVDR-MFP) algorithm incorporates the MVDR technique into the MFP algorithm to enhance beamforming performance. Such an adaptive MFP algorithm involves intensive computational and memory requirements due to its complex acoustic model and environmental adaptation. The real-time implementation of adaptive MFP algorithms for large surveillance areas presents a serious computational challenge where high-performance embedded computing and parallel processing may be required to meet real-time constraints. In this paper, three parallel algorithms based on domain decomposition techniques are presented for the MVDR-MFP algorithm on distributed array systems. The parallel performance factors in terms of execution times, communication times, parallel efficiencies, and memory capacities are examined on three potential distributed systems including two types of digital signal processor arrays and a cluster of personal computers. The performance results demonstrate that these parallel algorithms provide a feasible solution for real-time, scalable, and cost-effective adaptive beamforming on embedded, distributed array systems.