Fault Detection, Isolation, and Recovery techniques for large clusters of Inertial Measurement Units

Bittner, Drew E.; Christian, John A.; May, Darryl

doi:10.1109/plans.2014.6851379

Cited by 6 publications

(6 citation statements)

References 9 publications

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“…feasible to construct arrays with hundreds of sensing elements, and digitally process these measurements to create virtual high-performance sensors. The benefit of using an array of sensors is not only an increased measurement accuracy, but also an increased operation reliability thanks to the possibility of carrying out sensor fault detection and isolation [4], [5]. Further, from the redundant measurements the covariance of the measurement errors can be estimated and used to determine the reliability of the measurements [6].…”

Section: M M Mmentioning

confidence: 99%

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Inertial Sensor Arrays, Maximum Likelihood, and Cramér–Rao Bound

Skog

Nilsson

Händel

et al. 2016

IEEE Trans. Signal Process.

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Abstract-A maximum likelihood estimator for fusing the measurements in an inertial sensor array is presented. The maximum likelihood estimator is concentrated and an iterative solution method is presented for the resulting low-dimensional optimization problem. The Cramér-Rao bound for the corresponding measurement fusion problem is derived and used to assess the performance of the proposed method, as well as to analyze how the geometry of the array and sensor errors affect the accuracy of the measurement fusion. The angular velocity information gained from the accelerometers in the array is shown to be proportional to the square of the array dimension and to the square of the angular speed. In our simulations the proposed fusion method attains the Cramér-Rao bound and outperforms the current state-of-the-art method for measurement fusion in accelerometer arrays. Further, in contrast to the state-of-theart method that requires a 3D array to work, the proposed method also works for 2D arrays. The theoretical findings are compared to results from real-world experiments with an inhouse developed array that consists of 192 sensing elements.

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Section: M M Mmentioning

confidence: 99%

“…7. 4 More experimental results can be found in [50], where the performance of an earlier 4 Placement errors in the order of 0.1 mm are likely to arise in the PCB manufacturing and population process. revision of the array when used in a foot-mounted inertial navigation system were evaluated.…”

Section: B Experimentsmentioning

confidence: 99%

Inertial Sensor Arrays, Maximum Likelihood, and Cramér–Rao Bound

Skog

Nilsson

Händel

et al. 2016

IEEE Trans. Signal Process.

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show abstract

“…In addition, various performance indexes, that is, figures of merit (FOM) and test methods have been proposed in order to check the optimality of the configurations. For the FDI performances, the squared-error analysis [1], likelihood tests [10], [11], minimax analysis [12], mean-value test [13], parity test based FDI performance analysis [14], [15], wavelet transform based method [16], and many other algorithms [17]- [20] were proposed.…”

Section: A Backgroundmentioning

confidence: 99%

“…In order to derive the conditions for configurations minimizing the lever arm effect, two approaches can be taken. The first approach is to find both the measurement matrix H and the coefficient vector k that satisfy (20). This approach can provide a configuration minimizing the volume of the redundant sensor assembly by letting k j be the minimum deviation under physical constraints and by finding H which satisfies (20).…”

Section: B Constraints For Optimal Configuration Considering Lever Amentioning

confidence: 99%

“…The first approach is to find both the measurement matrix H and the coefficient vector k that satisfy (20). This approach can provide a configuration minimizing the volume of the redundant sensor assembly by letting k j be the minimum deviation under physical constraints and by finding H which satisfies (20). This approach, however, could not propose a optimal configuration that minimize the FOMs derived in the previous section, and therefore could not guarantee the best navigation performance.…”

Section: B Constraints For Optimal Configuration Considering Lever Amentioning

confidence: 99%

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Optimal Configuration of Redundant Inertial Sensors Considering Lever Arm Effect

Song

Park

2016

IEEE Sensors J.

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An optimal configuration for a redundant inertial measurement unit (RIMU) minimizing the lever arm effect and the constraints for the optimality are proposed. The proposed configuration provides the allocation of each sensor in an RIMU as well as the orientation, while conventional configurations, which do not consider the lever arm effect, suggest the orientation only. The orientation is fixed, so that the configuration may guarantee the best navigation performance, and the allocation of each sensor is determined for minimizing the lever arm effect and for the densest disposition of the structures. To qualify the navigation performance of a configuration, a novel figure of merit (FOM) indicating the worst navigation performance of an RIMU is proposed first. In general, an FOM, which indicates the averaged error covariance of all the estimation, is used as a navigation performance index. The proposed FOM, however, represents the worst error covariance, which contributes to the determination of a secure configuration against excessive noises. The optimal condition of the proposed FOM is also derived, in which case the proposed configuration guarantees the best navigation performance. Using the proposed FOM, the constraint for obtaining the optimal configuration, which guarantees the best navigation performance and minimizes the lever arm effect, is proposed. The effectiveness of the proposed method is confirmed via case studies and Monte Carlo simulations, which show that the RIMU with the proposed configuration sustains sufficiently high position accuracy in spite of the lever arm effect.Index Terms-Redundant sensors, figure of merit, navigation performance, lever arm effect, optimal configuration, allocation and orientation.

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Constant frequency drive method for MEMS for improved failure detection

Jamborhazi

Rencz

2015

2015 Symposium on Design, Test, Integration and Packaging of MEMS/MOEMS (DTIP)

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Fault Detection, Isolation, and Recovery techniques for large clusters of Inertial Measurement Units

Cited by 6 publications

References 9 publications

Inertial Sensor Arrays, Maximum Likelihood, and Cramér–Rao Bound

Inertial Sensor Arrays, Maximum Likelihood, and Cramér–Rao Bound

Optimal Configuration of Redundant Inertial Sensors Considering Lever Arm Effect

Constant frequency drive method for MEMS for improved failure detection

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