An Efficient and Robust Real-Time Calibration Routine for Inter-Sensor Offsets Within Integrated Multibeam Systems

Brandon Maingot
Ph.D. Thesis Proposal Defense

Ocean Engineering

Friday, Mar. 31, 2023, 12:00pm
Chase 130

While standard procedures ensuring accurate multi-sensor integration within a multibeam system already exist, operators are still plagued by small periodic systematic residuals in data. The source of these errors has long been recognized as the result of imperfect definitions of offsets, alignments, latencies and sound speed between the sonar and auxiliary sensors. In isolation, and shallow water geometry, any one of these sources is easy to recognize. As either water depth or the number of error sources increase, however, the resulting pattern of residuals can confound simple analysis. Gross errors are usually immediately apparent, but small errors such as a few milliseconds of latency or decimeters of lever arm, particularly in combination, can be hard to identify.

As a continuation of the Rigorous Inter-Sensor Calibrator (RISC), a theoretical model-based method previously developed, the approach has now been refined and applied to real-time data. The method relies on separating the bathymetric residuals in the seafloor data from the underlying truth in order to parametrically estimate the mismatch. Using a georeferencing model that explicitly includes the impact of six possible integration errors, the difference between the integrated surface, and a smoothed version of it, is minimized. Initial results have shown sensitivity of the method to variations in seafloor bathymetry and external systematic errors such as imperfect real time heave and water column modeling. To mitigate these issues more robust surface modelling and optimization techniques are proposed. To facilitate the real-time implementation of this tool, an improved multibeam integration model is also proposed.


Brandon Maingot completed his B.Sc. in Geomatics Engineering at the University of the West Indies in 2015. There he was introduced to hydrography, among other methods of spatial data collection and analysis. Brandon then entered the GEBCO program at the Center for Coastal and Ocean Mapping and received his Cat. A certificate in 2016. He continued at UNH to complete his master's degree in ocean engineering in 2019, emphasizing in ocean mapping. Brandon has since continued into the Ocean Engineering Ph.D. program. There he is researching efficient and robust automated sensor-offset calibration methods of bathymetric data. Brandon specializes in integration of remote sensing data in marine environments.