# A Wide-Area Deep Ocean Floor Mapping System: Design and Sea Tests
## 7 Future Work  
We plan to improve signal-processing and 3D image reconstruction algorithms by incorporating autofocus methods. We could also investigate merging these advances with our Bayesian uncertainty quantification and learning using our dynamically orthogonal stochastic acoustic wave predictions [30; 31]. A key aspect of the planned sea tests is the prediction of the ocean fields (e.g., sound speed, currents) to be used in estimating the bathymetry and seabed properties from the acoustic data and some temperature, salinity, and biogeochemical data. We could employ our MIT Multidisciplinary Simulation, Estimation, and Assimilation System (MSEAS) ocean modeling system [32; 33], optimal reduced-order theory and schemes for uncertainty quantification, and Bayesian data assimilation and learning [34; 35]. We could first predict joint probability density functions (PDFs) of the uncertain ocean and seabed fields, using our new coupled dynamically orthogonal stochastic ocean acoustic partial differential equations [36; 37; 38; 39], with extension to multiresolution probabilistic predictions. With these prior PDFs, our Bayesian inference algorithms then assimilate the measured acoustic data and sound speed profiles to jointly estimate posterior PDFs for the ocean fields, bathymetry, and seabed's geoacoustic properties [36; 40]. With Bayesian and machine learning [41; 42], we can then jointly learn better parameterizations (e.g., for scattering), neural model closures (e.g., for missing or unknown model terms), and even model formulations themselves (e.g., for model complexity).  
For the control and coordination of the ASVs, we could utilize our principled optimal path planning and coordination for operations in strong dynamic ocean currents and waves (e.g., [19; 43; 44]). With this model-based predictive control and path planning coordination, we could guide the ASV headings and speeds to ensure that the ASV remains in an optimal location with respect to the rest of the array. For multiple ASVs, our rigorous path planning and coordination methods could help maintain larger-scale patterns that are optimal for the inference of bathymetry while accounting for ocean currents and waves.  
Conceptualization and methodology, P.R., A.M. and P.F.J.L.; software, P.R., K.A., B.C., A.M., W.H.A. and A.C.; investigation and data curation, P.R., D.B. and A.M.; writing--original draft preparation and writing--review and editing, all authors; supervision and project administration, A.M. and P.F.J.L. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Under Secretary of Defense for Research and Engineering under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Under Secretary of Defense for Research and Engineering.  
Figure 17: The potential cost of an operational sparse-aperture sonar mapping using at least 25 ASVs and our current image reconstruction, focus, and relative navigation techniques.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding authors.  
**Acknowledgments:** We thank [PERSON] of MIT Lincoln Laboratory for assistance with data acquisition and data analysis, [PERSON] of MIT Lincoln Laboratory for exceptional design of the apparatuses, and our colleagues on the MIT Lincoln Laboratory Autonomous Systems Line and Integrated Systems Line Committees for their helpful technical advice.  
**Conflicts of Interest:** The authors declare no conflict of interest.