Advances in multibeam sonar mapping and data visualization have increasingly brought to light the subtle integration errors remaining in bathymetric datasets. Traditional field calibration procedures, such as the patch test, just account for static orientation bias and sonar-to-position latency. This, however, ignores the generally subtler integration problems that generate timevarying depth errors. Such dynamic depth errors are the result of an unknown offset in one or more of orientation, space, sound speed or time between the sonar and ancillary sensors. Such errors are systematic, and thus should be predictable, based on their relationship between the input data and integrated output. A first attempt at addressing this problem utilized correlations between motion and temporally smoothed, ping-averaged residuals. The known limitations of that approach, however, included only being able to estimate the dominant integration error, imperfectly accounting for irregularly spaced sounding distribution and only working in shallow water. 

This thesis presents a new and improved means of considering the dynamics of the integration error signatures which can address multiple issues simultaneously, better account for along-track sounding distribution, and is not restricted to shallow water geometry. The motion-driven signatures of six common errors are simultaneously identified. This is achieved through individually considering each sounding’s input-error relationship along extended sections of a single swath corridor. Such an approach provides a means of underway system optimization using nothing more than the bathymetry of typical seafloors acquired during transit. Initial results of the new algorithm are presented using data generated from a simulator, with known inputs and integration errors, to test the efficacy of the method. Results indicate that successful estimation requires conditions of significant vessel motion over periods of a few tens of seconds as well as smooth, gently rolling bathymetry along the equivalent spatial extent covered by the moving survey platform.