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Advanced sonar algorithms with complex-wave propagation processing are of critical importance in the field of acoustic signal processing, as they possess the ability to localize signal sources more precisely in a cluttered environment. Conventional Matched-Field Processing (CMFP) is such an algorithm that provides range and depth results by employing environmental parameters. However, the enhancement of features is limited by the extensive computational and memory requirements of the algorithm even for a small problem size. High-performance computing, when applied to matched-field processing on a microprocessor-based distributed system, can provide solutions for this challenging problem in performance, scalability, and cost. Based on domain decomposition techniques, two parallel algorithms for matched-field processing are introduced in this paper for in-situ signal processing. Performance results collected on low-power distributed embedded systems are presented in terms of execution times and parallel efficiencies. The results of these analyses demonstrate that parallel in-situ processing holds the potential to meet the needs of matched-field processing in a scalable fashion.