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Imaging and mapping sonars such as forward-looking sonars (FLS) and side-scan sonars (SSS) are sensors frequently used onboard autonomous underwater vehicles. To acquire information from around the vehicle, it is desirable for these sonar systems to insonify a large area; thus, the sonar transmit beampattern should have a wide field of view. In this work, we study the problem of the optimization of wide transmission beampatterns. We consider the conventional phased-array beampattern design problem where all array elements transmit an identical waveform. The complex weight vector is adjusted to create the desired beampattern shape. In our experiments, we consider wide transmission beampatterns (>= 20 degrees) with uniform output power. In this paper, we introduce a new iterative-convex optimization method for narrowband linear phased arrays and compare it to existing approaches for convex and concave-convex optimization. In the iterative-convex method, the phase of the weight parameters is allowed to be complex as in disciplined convex-concave programming (DCCP). Comparing the iterative-convex optimization method and DCCP to the standard convex optimization, we see that the former methods archive optimized beampatterns closer to the desired beampatterns. Furthermore, for the same number of iterations, the proposed iterative-convex method achieves optimized beampatterns, which are closer to the desired beampattern than the beampatterns achieved by optimization with DCCP., QC 20221031

The increased frequency of floods due to global warming has posed formidable challenges for rescue operations worldwide. Standard mapping tools, including Google Maps and OpenStreetMap, are infeasible for post-flood scenarios due to the destruction of known structures and road networks. While response teams often use satellite and drone imagery to aid flood relief efforts, they typically lack the ability to detect underwater obstacles, making it unsafe for rescue boats to navigate. Furthermore, the complex nature of post-flood environments, such as highly variable depth, random unstructured obstacles, and extremely turbid water due to sand particles, increases the need for robust environment perception and mapping systems tailored to the complexities of post-flood environments. Additionally, a significant challenge in developing such a system arises from the unavailability of comprehensive datasets about flooded environments, a limitation that has constrained previous research efforts. The primary objective of this thesis is to provide a robust surface and underwater perception system that uses rich multi-modal sensory knowledge and provides traversability information for safe navigation. This perception system involves comprehensive understanding of obstacle's characteristics and categorizing their threat levels by integrating surface and underwater sensory data. Additionally, this research attempts to devise a versatile system capable of seamless reconfiguration across various surface water vehicles. This adaptability benefits rescue teams equipped with boats featuring diverse kinematics and motion models. Consequently, this thesis proposed a novel Shallow Water traversabIlity Mapping (SWiM) architecture, which integrates multiple sensory modalities to create a lightweight 2.5-dimensional traversability map covering both surface and underwater modalities. Enhancements in obstacle detection within the underwater environment, utilizing low Signal-to-Noise Ratio (SNR

Underwater simulators are less common due to the complexity of underwater acoustics. Simulation is an effective tool for rapid testing of autonomous vehicles and complements the test and evaluation process. The goal of this thesis is to present a computationally efficient forward-looking sonar simulation model for robotic applications. A model for a single sonar beam is developed using a point-scattering model, applying both Fourier synthesis and a correction for beam forming. The single sonar beams are concatenated to simulate a forward-looking sonar system field of view. The result is a sonar simulation model that can be used in the established ROS Gazebo robotic framework as a tool for effective testing of autonomous underwater vehicles. Future improvements in the acoustics of the sonar model include the addition of reverberation, multi-path propagation, and interference., Lieutenant, United States Navy, Approved for public release. distribution is unlimited