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This article addresses the problem of dynamic online estimation and compensation of hard-iron and soft-iron biases of three-axis magnetometers under dynamic motion in field robotics, utilizing only biased measurements from a three-axis magnetometer and a three-axis angular rate sensor. The proposed magnetometer and angular velocity bias estimator (MAVBE) utilizes a 15-state process model encoding the nonlinear process dynamics for the magnetometer signal subject to angular velocity excursions, while simultaneously estimating nine magnetometer bias parameters and three angular rate sensor bias parameters, within an extended Kalman filter framework. Bias parameter local observability is numerically evaluated. The bias-compensated signals, together with three-axis accelerometer signals, are utilized to estimate bias-compensated magnetic geodetic heading. Performance of the proposed MAVBE method is evaluated in comparison to the widely cited magnetometer-only TWOSTEP method in numerical simulations, laboratory experiments, and full-scale field trials of an instrumented autonomous underwater vehicle in the Chesapeake Bay, Maryland, USA. For the proposed MAVBE, (i) instrument attitude is not required to estimate biases, and the results show that (ii) the biases are locally observable, (iii) the bias estimates converge rapidly to true bias parameters, (iv) only modest instrument excitation is required for bias estimate convergence, and (v) compensation for magnetometer hard-iron and soft-iron biases dramatically improves dynamic heading estimation accuracy. 

This paper addresses the problem of ice-relative underwater robotic vehicle navigation relative to moving or stationary contiguous sea ice. A review of previously-reported under-ice navigation methods is given, as well as motivation for the use of under-ice robotic vehicles with precision navigation capabilities. We then describe our proposed approach, which employs two or more satellite navigation beacons atop the sea ice along with other precision vehicle and ship mounted navigation sensors to estimate vehicle, ice, and ship states by means of an Extended Kalman Filter. A performances sensitivity analysis for a simulated 7.7 km under ice survey is reported. The number and the location of ice deployed satellite beacons, rotational and translational ice velocity, and separation of ship-based acoustic range sensors are varied, and their effects on estimate error and uncertainty are examined. Results suggest that increasing the number and/or separation of ice-deployed satellite beacons reduces estimate uncertainty, whereas increasing separation of ship-based acoustic range sensors has little impact on estimate uncertainty. Decreasing ice velocity is also correlated with reduced estimate uncertainty. Our analysis suggests that the proposed method is feasible and can offer scientifically useful navigation accuracy over a range of operating conditions. 

This article reports an adaptive sensor bias observer and attitude observer operating directly on [Formula: see text] for true-north gyrocompass systems that utilize six-degree-of-freedom inertial measurement units (IMUs) with three-axis accelerometers and three-axis angular rate gyroscopes (without magnetometers). Most present-day low-cost robotic vehicles employ attitude estimation systems that employ microelectromechanical system (MEMS) magnetometers, angular rate gyros, and accelerometers to estimate magnetic attitude (roll, pitch, and magnetic heading) with limited heading accuracy. Present-day MEMS gyros are not sensitive enough to dynamically detect the Earth’s rotation, and thus cannot be used to estimate true-north geodetic heading. Relying on magnetic compasses can be problematic for vehicles that operate in environments with magnetic anomalies and those requiring high-accuracy navigation as the limited accuracy ([Formula: see text] error) of magnetic compasses is typically the largest error source in underwater vehicle navigation systems. Moreover, magnetic compasses need to undergo time-consuming recalibration for hard-iron and soft-iron errors every time a vehicle is reconfigured with a new instrument or other payload, as very frequently occurs on oceanographic marine vehicles. In contrast, the gyrocompass system reported herein utilizes fiber optic gyroscope (FOG) IMU angular rate gyro and MEMS accelerometer measurements (without magnetometers) to dynamically estimate the instrument’s time-varying true-north attitude (roll, pitch, and geodetic heading) in real-time while the instrument is subject to a priori unknown rotations. This gyrocompass system is immune to magnetic anomalies and does not require recalibration every time a new payload is added to or removed from the vehicle. Stability proofs for the reported bias and attitude observers, preliminary simulations, and a full-scale vehicle trial are reported that suggest the viability of the true-north gyrocompass system to provide dynamic real-time true-north heading, pitch, and roll utilizing a comparatively low-cost FOG IMU.