Attitude and Rate Sensor Bias Estimation for Underwater Vehicles

Jørgensen, Erlend Kvinge; Schjelberg, Ingrid

doi:10.1109/med.2018.8442816

Cited by 2 publications

(8 citation statements)

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“…Subsystem Σ 2 is an observer based on the XKF principle, suggested in [2], estimating attitude and rate sensor bias, based on an estimate of p n and β, and pseudorange difference and rate sensor measurements. For more details regarding Σ 2 , see Appendix A-B.…”

Section: B Subsystem σ 2 : Estimate θ B ωmentioning

confidence: 99%

“…Proof: See [2] for proof of the nominal system, combined with Lemma 1 in which Assumption 3 ensures that 2 where z is the output from Σ 1 . Unfortunately, it is impossible to obtain global stability properties due to the singularities θ = π/2 + kπ, k ∈ Z.…”

Section: B Subsystem σ 2 : Estimate θ B ωmentioning

confidence: 99%

“…B. Subsystem Σ 2 : Attitude XKF Subsystem Σ 2 is based on the observer suggested in [2]. The observer estimates attitude and rate sensor bias using acoustic and rate sensor measurements, which is designed using the XKF principle, and consists of three stages when a position estimate is available.…”

Section: Appendix a Observer Detailsmentioning

confidence: 99%

“…By employing DTOA measurements, it is, as shown in [2], possible to estimate attitude using none of the accelerometer, magnetometer, and gyrocompass measurements. A similar approach can be found in [28], in which an NLO is proposed, based on a combination of LBL, USBL, and rate sensor measurements.…”

mentioning

confidence: 99%

“…This article builds on the work in [29] and [30], where an observer is suggested for determining position, velocity, and AWS using LBL and depth measurements. It also builds on the work in [2], where a locally exponentially stable (LES) observer with easily identifiable singular points is suggested, estimating attitude and rate sensor bias using only a position estimate and TDOA and rate sensor measurements, and [1], in which the accelerometer measurement is used to estimate roll and pitch. These two observers are used as parts of the proposed observer.…”

mentioning

confidence: 99%

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Underwater Position and Attitude Estimation Using Acoustic, Inertial, and Depth Measurements

Jørgensen

Fossen

Bryne

et al. 2020

IEEE J. Oceanic Eng.

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This article considers the problem of constructing an observer for estimating position, velocity, attitude, underwater wave speed, rate sensor bias, and accelerometer bias that has both proven stability and close-to-optimal performance with respect to noise. The observer takes pseudorange, pseudorange difference, depth, and inertial measurements as input, and has a cascade structure for which the equilibrium point is proven to be locally exponentially stable due to the singularities in the attitude representation. The design of the observer is based on the exogenous Kalman filter principle, in which estimators with proven stability provide a linearization point for a linearized Kalman filter, to achieve both proven stability and close-to-optimal noise properties. Experimental validation is provided, with ground truth values generated by a camera positioning system with millimeter accuracy. The observer is compared to an extended Kalman filter and to a nonimplementable linearized Kalman filter using the true state as the linearization point, and the estimation error is almost identical to the linearized Kalman filter using the true state as a linearization point.

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Section: B Subsystem σ 2 : Estimate θ B ωmentioning

confidence: 99%

Section: B Subsystem σ 2 : Estimate θ B ωmentioning

confidence: 99%

Section: Appendix a Observer Detailsmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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