Demonstration of advanced sensors for underwater unexploded ordnance (UXO) detection

Clem, T.R.; Sternlicht, Daniel D.; Fernandez, Jose E.; Prater, James L.; Holtzapple, Richard; Gibson, Ray; Klose, John P.; Marston, Timothy M.

doi:10.1109/oceans.2012.6405131

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…The development of a new generation of small low-cost underwater vehicles (UVs) has begun to enable oceanographic, environmental assessment, and national security missions that were previously considered impractical or infeasible (Clegg and Peterson, 2003;Clem et al, 2012;Corke et al, 2007;Dunbabin et al, 2005;Packard et al, 2013;Steele et al, 2012;Zhou et al, 2014). These small lowcost robotic vehicles commonly employ micro-electro-mechanical systems (MEMS) IMUs comprised of 3-axis MEMS magnetometers, angular rate sensors, and accelerometers to estimate local magnetic heading, pitch, and roll, typically to within several degrees of accuracy, but require careful soft-iron and hard-iron calibration and compensation to achieve these accuracies (Crassidis et al, 2007;Guo et al, 2008;Hamel and Mahony, 2006;Mahony et al, 2008Mahony et al, , 2012Metni et al, 2005Metni et al, , 2006Wu et al, 2015).…”

Section: A Background and Motivationmentioning

confidence: 99%

Online 3-Axis Magnetometer Hard-Iron and Soft-Iron Bias and Angular Velocity Sensor Bias Estimation Using Angular Velocity Sensors for Improved Dynamic Heading Accuracy

Spielvogel¹,

Shah²,

Whitcomb³

2022

Preprint

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This article addresses the problem of dynamic on-line estimation and compensation of hard-iron and softiron biases of 3-axis magnetometers under dynamic motion in field robotics, utilizing only biased measurements from a 3-axis magnetometer and a 3-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 9 magnetometer bias parameters and 3 angular rate sensor bias parameters, within an extended Kalman filter framework. Bias parameter local observability is numerically evaluated. The bias-compensated signals, together with 3-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, MD, 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.

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Section: A Background and Motivationmentioning

confidence: 99%

Online 3-Axis Magnetometer Hard-Iron and Soft-Iron Bias and Angular Velocity Sensor Bias Estimation Using Angular Velocity Sensors for Improved Dynamic Heading Accuracy

Spielvogel¹,

Shah²,

Whitcomb³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The development of a new generation of small low-cost underwater vehicles (UVs) has begun to enable oceanographic, environmental assessment and national security missions that were previously considered impractical or infeasible (Clegg and Peterson, 2003;Clem et al, 2012;Corke et al, 2007;Dunbabin et al, 2005;Packard et al, 2013;Steele et al, 2012;Zhou et al, 2014). These small low-cost robotic vehicles commonly employ microelectromechanical systems (MEMS) IMUs comprised of three-axis MEMS magnetometers, angular rate sensors, and accelerometers to estimate local magnetic heading, pitch, and roll, typically to within several degrees of accuracy, but require careful soft-iron and hard-iron calibration and compensation to achieve these accuracies (Crassidis et al, 2007;Guo et al, 2008;Hamel and Mahony, 2006;Mahony et al, 2008Mahony et al, , 2012Metni et al, 2005Metni et al, , 2006Wu et al, 2015).…”

Section: Background and Motivationmentioning

confidence: 99%

Online Three-Axis Magnetometer Hard-Iron and Soft-Iron Bias and Angular Velocity Sensor Bias Estimation Using Angular Velocity Sensors for Improved Dynamic Heading Accuracy

Spielvogel¹,

Shah²,

Whitcomb³

2022

View full text Add to dashboard Cite

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.

show abstract

“…Over the past decade the development of a new generation of small low-cost underwater vehicles (UVs) has begun to enable oceanographic, environmental assessment, and national security missions that were previously considered impractical or infeasible (Clegg and Peterson, 2003; Clem et al, 2012; Packard et al, 2013; Steele et al, 2012; Zhou et al, 2014: e.g.,). This new generation of UVs often employ low-cost navigation systems that presently limit them to missions requiring only low-precision navigation of O (1–100) m accuracy when submerged.…”

Section: Introductionmentioning

confidence: 99%

Adaptive bias and attitude observer on the special orthogonal group for true-north gyrocompass systems: Theory and preliminary results

Spielvogel

Whitcomb

2019

The International Journal of Robotics Research

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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.

show abstract

Demonstration of advanced sensors for underwater unexploded ordnance (UXO) detection

Cited by 7 publications

References 4 publications

Online 3-Axis Magnetometer Hard-Iron and Soft-Iron Bias and Angular Velocity Sensor Bias Estimation Using Angular Velocity Sensors for Improved Dynamic Heading Accuracy

Online 3-Axis Magnetometer Hard-Iron and Soft-Iron Bias and Angular Velocity Sensor Bias Estimation Using Angular Velocity Sensors for Improved Dynamic Heading Accuracy

Online Three-Axis Magnetometer Hard-Iron and Soft-Iron Bias and Angular Velocity Sensor Bias Estimation Using Angular Velocity Sensors for Improved Dynamic Heading Accuracy

Adaptive bias and attitude observer on the special orthogonal group for true-north gyrocompass systems: Theory and preliminary results

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