Parallel and fault-tolerant techniques for adaptive matched-field processing (MFP) on distributed array systems

Sonar array systems in the highly cluttered undersea environment require new and enhanced signal-processing algorithms and sophisticated architectures to meet the stringent real-time and reliability mission requirements. These demands present significant challenges in terms of computational complexity and dependability; and necessitate the development and use of high-performance computing techniques and advanced signal-processing algorithms. Our study introduces novel parallel and fault-tolerant techniques for adaptive matched-field processing (MFP) which we investigated on distributed array systems along with high-performance networking and distributed computing architectures. We developed the parallel algorithms for adaptive MFP to satisfy the computational and memory requirements not readily supported by cutting-edge, high-performance, single-processor systems. We proposed robust MFP algorithms to compensate for the performance degradation presented by sensor failures in sonar array systems. We also developed fault-tolerant parallel algorithms characterized by low overhead and fast recovery time to enhance dependability. We implemented the proposed parallel algorithms and fault-tolerant mechanisms on scalable clusters of computers. We compared their performance considering several critical factors such as parallel and fault-tolerant techniques, communication schemes, and processor architectures. Results show that these parallel and fault-tolerant algorithms can provide a feasible solution for real-time, scalable, and highly reliable beamforming implementations on distributed array systems.