# A Wide-Area Deep Ocean Floor Mapping System: Design and Sea Tests
## 1 Introduction
However, these challenges can be mitigated using novel signal processing tools and acoustic propagation and imaging algorithms, including Bayesian and artificial intelligence (AI) schemes, enabling sparse arrays to achieve higher resolution. In addition, the ocean sound speed field needs to be estimated through the full water column, and the motions of the ASVs need to be measured very precisely. A surface-based sparse sonar system will need to be integrated with real-time, multiresolution probabilistic ocean field estimation, optimal ASV path planning and coordination, and Bayesian inversions and machine learning of ocean physics and acoustics fields [16; 17; 18; 19]. Figure 1: The autonomous sparse-aperture concept offers a potential 100-fold improvement in resolution compared with existing surface ship multibeam sonar, or a potential 50-fold increase in coverage rate compared with subsea sonar. Deploying several copies of this autonomous swarm of ASVs to map the deep ocean seabed at high resolution would enable data collection that could revolutionize our understanding and modeling of the ocean. The area coverage rate--estimated from sensor swath and survey speed--currently achievable for high-resolution mapping with an autonomous underwater vessel is approximately 6 km\({}^{2}\)/h, whereas a surface vessel maps at more than 50\(\times\) that rate, albeit only with low resolution. Efforts are underway to incrementally improve the efficiency of undersea-vehicle-based surveys--for instance, the recently developed PISCES system employs both surface and undersea sensors in a novel bistatic configuration [20]. Our system processes bistatic target reflections with a large sparse aperture operating from the surface and thus is capable of achieving a much greater coverage rate. Autonomous systems enable cost-effective, extended-duration data collections that can map large areas of the deep ocean bottom. For a sparse-aperture system, the ocean field estimation, ASV path planning and coordination, Bayesian and AI inversions, and signal processing could be completed either onboard the ASVs or remotely. For example, the ASVs could be deployed from a ship, controlled and guided remotely, and left to map the seabed (including searching for a particular object on the seabed) for months until maintenance is needed. All collected data would be returned through a satellite link, and a high-resolution map of the seabed could be generated remotely. The long-term goal is to demonstrate that a small swarm of ASVs can achieve high-resolution maps of the seabed from the ocean surface. This work addresses two specific objectives:  
1. To demonstrate that a sonar array composed of multiple collaborative ASVs can produce a high-resolution map of the seabed in deep water with precise relative positioning knowledge;
2. To demonstrate that a single ASV working in conjunction with a large sonar array can achieve all position, timing, and acoustic signal requirements to work effectively as part of the large array. Our teams at the Lincoln Laboratory (LL) and Massachusetts Institute of Technology (MIT) have started working toward achieving these two goals. In particular, design studies and risk-reduction efforts for the overall project have been performed, including an analysis of the concept; a demonstration of a sparse aperture in a tank; the development of a low-cost, ASV-sized sonar array; sea testing of a large sonar array; the fabrication of a first-generation ASV; and initial relative navigation algorithm development. Figure 2 shows an overview of the technological development phases. This paper describes the design and specifications of our ocean floor mapping system and the techniques used for data acquisition, signal processing, and imaging. We illustrate the system's capabilities using a proof-of-principle tank test at MIT LL Autonomous Systems Development Facility and an ocean test in Boston Harbor.