Attitude and Gyro bias estimation using range-difference and IMU measurements

Jørgensen, Erlend Kvinge; Schjølberg, Ingrid

doi:10.1109/auv.2016.7778659

Cited by 4 publications

(9 citation statements)

References 15 publications

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“…Illustration of system with range-difference measurements. Variables are defined and explained in Section II-C [1] setup, calibration of equipment, and implementation of the observer. Section V provides the results from the lab experiments.…”

Section: Outlinementioning

confidence: 99%

“…where y,i is the zero-mean Gaussian white noise with variance σ 2 y,i and β > 0 is an unknown multiplicative parameter to take into account uncertainty in the underwater AWS. The DTOA measurements are found on subtracting pseudoranges, based on the formulation in [1]. Each pseudorange difference δ i,j is modeled as…”

Section: Measurement Modelsmentioning

confidence: 99%

“…Naturally, an offset in measurements will decrease accuracy, depending on how fast the dynamic of the system is, as the measurements are assumed to be simultaneous, and delayed measurements will give inaccurate information. However, it is important to note that simulations done in [1], [29], and [31] indicate that in general for the XKF, the linearization point provided by the auxiliary estimator can be somewhat inaccurate, depending on system configuration and noise magnitude, and the output of the observer will still be close-to-optimal w.r.t. noise.…”

Section: Implementation Aspectsmentioning

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%

“…These two observers are used as parts of the proposed observer. Furthermore, the proposed observer relaxes the assumption made in [1], that accelerometer bias is known, by estimating accelerometer bias online, and an observer for determining a rough bias estimate used as a linearization point is suggested.…”

mentioning

confidence: 99%

See 4 more Smart Citations

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: Outlinementioning

confidence: 99%

Section: Measurement Modelsmentioning

confidence: 99%