Advances in In Situ Alignment Calibration of Doppler and High/Low‐end Attitude Sensors for Underwater Vehicle Navigation: Theory and Experimental Evaluation

Abstract

This paper reports the development and comparative performance evaluation, using laboratory and at‐sea field data, of new methods for the problem of in situ calibration of the alignment rotation matrix between Doppler sonar velocity sensors and attitude sensors arising in the navigation of underwater vehicles. Most previously reported solutions to this alignment calibration problem require the use of absolute navigation fixes of the underwater vehicle, thus requiring additional navigation sensors and/or beacons to be located externally and apart from the underwater vehicle. We report four novel alignment calibration methods employing only internal vehicle navigation sensors for velocity, acceleration, attitude, and depth. We report the results of comparative analysis of the performance of these new methods and a previously reported method with a navigation laboratory and at‐sea field data. Laboratory data were obtained with the Johns Hopkins University JHU remotely operated underwater vehicle in the JHU Hydrodynamic Test Facility. At‐sea field data were obtained from deep‐water survey missions of the Sentry autonomous underwater vehicle conducted in March, 2011 in the Kermadec Arc in the Southern Pacific Ocean. In addition, we report a comparative experimental evaluation of several recently reported calibration methods when employing low‐cost microelectromechanical system attitude sensors. In all these cases, the results reveal consistent differences in performance of the various methods when analyzed on navigation data from several different vehicle dives.

An Alignment Method for Strapdown Inertial Navigation Systems Assisted by Doppler Radar on a Vehicle-Borne Moving Base

“…The speed measurement error of the Doppler radar is mainly caused by the scale factor error and installation errors [33,34,35,36]. Here, the above errors must be mathematically modeled.…”

Performance Enhancement of a USV INS/CNS/DVL Integration Navigation System Based on an Adaptive Information Sharing Factor Federated Filter

“…Sound speed, installation error and ocean currents are the main DVL error factors, and most of the DVL errors caused by these factors can be calculated and compensated before DVL velocity output. However, not all DVL velocity errors can be compensated, which means that there is a residual error as the component of DVL velocity output and influence the DVL accuracy [ 36 , 37 , 50 , 51 , 52 ]. It means that the components of DVL output includes the vehicle’s real velocity and the DVL residue error, and the DVL residue error is unclear where it comes from.…”

“…However, the integration accuracy based on DVL/INS can be influenced by some resources, for example, the rotation matrix between DVL and INS, the DVL measurement error and so forth. Troni [ 37 ] proposed the development of new methods for the problem of in situ calibration of the alignment rotation matrix between Doppler sonar velocity sensors and attitude sensors arising in the navigation of underwater vehicles and performed a comparative performance evaluation, using laboratory and at-sea field data. References [ 38 , 39 ] show the methods of calibrating the DVL measurement error, which is caused by the DVL itself, ocean currents and so on.…”

SINS/DVL integrated based on interacting multiple model approach by integrating feedback particle filter for ship navigation

“…As a standalone system with high precision in short period and high data rate, INS can provide accurate position, velocity, and attitude by the measurements of accelerometers and gyroscopes, however, the bias errors of INS solution can grow unbounded with time elapsing [1]- [3]. The DVL is a common navigation instrument for a vessel, which can provide velocity by the principle of Doppler effects [4]. INS/DVL integrated navigation system can be employed to mitigate the error growth of the INS, which is deemed as one of the most potential method for precision long-range navigation for unmanned surface vessels.…”