Precision Attitude Determination Using a Multiple Model Adaptive Estimation Scheme

Lam, Quang M.

doi:10.1109/aero.2007.352657

Cited by 27 publications

(20 citation statements)

References 5 publications

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“…In particular, the auto-correlation will increase with decreasing KF bandwidth. The reason can be explained by the discrete-time KF of Equation (17). The reason lies in that the KF will mainly depend on the estimated value of ˆk X rather than measurement information From the above results, it can be seen that the performance of KF and measurement precision of estimated rate signal are seriously related to the KF bandwidth, namely, it is directly related to the values of parameter qω.…”

Section: Static Drift Test Resultsmentioning

confidence: 94%

“…In this paper, the errors for MEMS gyroscope only consider the dominant stochastic errors without deterministic errors since those can be compensated by testing procedure. Numerous experiments have demonstrated that the noise of the rate random walk (RRW) and angular random walk (ARW) are considered the most dominant error sources, consequently, here a typical error model is shown to describe the gyroscope [17,18]:…”

Section: Modeling Of Stochastic Error For Mems Gyroscopementioning

confidence: 99%

“…Thus the optimal estimates of the true angular rate and bias drift can be obtained by Equation (17). The discrete-time KF is implemented as shown in Figure 2.…”

mentioning

confidence: 99%

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Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal

et al. 2015

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Abstract:In this paper, a novel approach for processing the outputs signal of the microelectromechanical systems (MEMS) gyroscopes was presented to reduce the bias drift and noise. The principle for the noise reduction was presented, and an optimal Kalman filter (KF) was designed by a steady-state filter gain obtained from the analysis of KF observability. In particular, the true angular rate signal was directly modeled to obtain an optimal estimate and make a self-compensation for the gyroscope without needing other sensor's information, whether in static or dynamic condition. A linear fit equation that describes the relationship between the KF bandwidth and modeling parameter of true angular rate was derived from the analysis of KF frequency response. The test results indicated that the MEMS gyroscope having an ARW noise of 4.87°/h 0.5 and a bias instability of 44.41°/h were reduced to 0.4°/h 0.5 and 4.13°/h by the KF under a given bandwidth (10 Hz), respectively. The 1σ estimated error was reduced from 1.9°/s to 0.14°/s and 1.7°/s to 0.5°/s in the constant rate test and swing rate test, respectively. It also showed that the filtered angular rate signal could well reflect the dynamic characteristic of the input rate signal in dynamic conditions. The presented algorithm is proved to be effective at improving the measurement precision of the MEMS gyroscope. OPEN ACCESSMicromachines 2015, 6 267

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Section: Static Drift Test Resultsmentioning

confidence: 94%

Section: Modeling Of Stochastic Error For Mems Gyroscopementioning

confidence: 99%

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Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal

et al. 2015

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“…A 6 state attitude determination filter which contains three attitude errors and three gyro bias error states is proven to be robust during low rate operation but cannot correct for gyro scale factor misalignment error effects during high rate operation 6 . It is shown that under high rate conditions, gyro scale factor and misalignment errors strongly degrade the 6-state filter performance 6 .…”

Section: Sensor Modelmentioning

confidence: 99%

“…It is shown that under high rate conditions, gyro scale factor and misalignment errors strongly degrade the 6-state filter performance 6 . The gyroscope sensor model is given as shown in (23), with scale factor and misalignment represents the zero mean Gaussian white noise from the scale factor, and misalignment errors respectively.…”

Section: Sensor Modelmentioning

confidence: 99%

In-drilling alignment scheme for borehole assembly trajectory tracking over a wireless ad hoc network

Odei-Lartey

Hartmann

2016

SPIE Proceedings

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This paper describes the feasibility and implementation of an In-drilling alignment method for an inertia navigation system based measurement while drilling system as applied to the vertical drilling process, where there is seldom direct access to a reference measurement due the communication network architecture and telemetry framework constraints. It involves the sequential measurement update of multiple nodes by the propagation of the reference measurement sequentially over nodes, each of which has an integrated inertia measurement unit sensor and runs the extended Kalman filter based signal processing unit, until the final measurement update of the main sensor node embedded in the borehole assembly is done. This is done particularly to keep track of the yaw angle position during the drilling process.

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An adaptive filter method for spacecraft using gravity assist

et al. 2015

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Precision Attitude Determination Using a Multiple Model Adaptive Estimation Scheme

Cited by 27 publications

References 5 publications

Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal

Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal

In-drilling alignment scheme for borehole assembly trajectory tracking over a wireless ad hoc network

An adaptive filter method for spacecraft using gravity assist

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