| Underwater acoustic signal processing has various applications in the underwater sensing systems, including the underwater acoustic communication (UAC) system and the active sonar system. A well-designed UAC system can achieve reliable and high data-rate communication to facilitate the operation of submarines, undersea sensors, and unmanned undersea vehicles (UUVs) while active sonar system demands effective and efficient signal processing techniques to accurately detect, localize, and even track the target of interest. The focus of this dissertation is to use the appropriate signal processing techniques to design such UAC and active sonar systems.;For the former, we focus on designing a mobile multi-input multi-output (MIMO) UAC system over double-selective channels subject to both inter-symbol interference and Doppler scaling effects. Temporal resampling is implemented to effectively convert the Doppler scaling effects to Doppler frequency shifts. By simplifying the assumption on the Doppler frequency shifts imposed on the channel taps across all the transmitter and receiver pairs, two sparse channel estimation algorithms, both as an extension of the original sparse learning via iterative minimization (SLIM) method, are proposed for channel estimation. Regarding symbol detection, we employ Turbo equalization and propose a fast implementation of the standard Turbo equalizer for retrieving the transmitted signal. The effectiveness of the considered mobile MIMO UAC scheme is demonstrated using both simulated data and measurements recently acquired during the MACE10 in-water experiment.;For the latter, we consider a multistatic active sonar system that employs multiple stationary transmitters and receivers. Two signal processing aspects related to such a system design are addressed, namely target range-Doppler imaging and target parameter estimation. To enhance the range-Doppler imaging performance, a hybrid dense-sparse method is proposed to improve resolution and reduce sidelobe levels simultaneously while maintaining high accuracy. In the presence of multiple targets, each peak of the range-Doppler images need to be associated with a specified target before the target parameter estimation. To efficiently solve this problem, we develop a generalized K-Means clustering (GKC) method, which iteratively assigns peaks to targets and then estimates the target parameters based on the current association pattern. Moreover, based on fact that different transmitter-receiver pairs have different reflection coefficients, we develop a weighted least-squares method where the target parameters are refined in an iterative manner using weighting. Note that if each of the receivers is equipped with a large array that can provide accurate angle estimates of the targets, the peak association problem becomes an easy problem or even disappears entirely. For such case, the active sonar system demands an advanced source localization method. However, most existing techniques are developed under the narrowband assumption, which becomes invalid due to the nature of the sonar application. We thus develop two extensions of the SLIM algorithm to solve the wideband source localization problem, which will be demonstrated to provide satisfactory performance. |
