Data Fusion Algorithms for Multiple Inertial Measurement Units

Bancroft, Jared B.; Lachapelle, Gérard

doi:10.3390/s110706771

Cited by 110 publications

(82 citation statements)

References 17 publications

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“…For the H, the constraint for the allocation gives Under these constraints, the distances d i j from i -th sensor to j -th sensor become (29), as shown at the bottom of this page.…”

Section: A Case Studies For the Optimal And Suboptimal Configurationsmentioning

confidence: 99%

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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“…For the H, the constraint for the allocation gives Under these constraints, the distances d i j from i -th sensor to j -th sensor become (29), as shown at the bottom of this page.…”

Section: A Case Studies For the Optimal And Suboptimal Configurationsmentioning

confidence: 99%

Optimal Configuration of Redundant Inertial Sensors Considering Lever Arm Effect

Song

Park

2016

IEEE Sensors J.

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“…(13) i= l where 0: = o:'.6.X i and defining P* (X i ) p'(X i ).6.X i p(X i )/o:', then (11) and (12) can be rewritten as: N Xc = L X i' P*( X i ) (14) i= l and (15) i= l…”

Section: B Probability Density Function Truncationmentioning

confidence: 99%

“…Other techniques use the IMU in the foot and the antenna in another part of the body [11], [12], and increase the covariance of the measurement to account for the unknown relative position between navigation points. Other authors [13], [14] use a model of the lever arm but as the real relative position is not known, this might affect the dynamics of the navigation points.…”

mentioning

confidence: 99%

A constraint approach for UWB and PDR fusion

Zampella

Angelis

Skog

et al. 2012

2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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Pedestrian Dead-Reckoning (PDR) and Radio Fre quency (RF) ranging/positioning are complementary techniques for position estimation but they usually locate different points in the body (RF in the head/hand and PDR in the foot). We propose to fuse the information from both navigation points using a constraint filter with an upper bound in the distance between the estimated positions of both sensors.For a pedestrian with an IMU for PDR in the foot and a RF positioning system in the head, the simplest bound is a maximal distance of 2 m between the positions of the sensors, this establish a spherical limit to the difference in the positions. It is also possible to use a smaller, non-symmetrical bound that establishes an ellipsoid as the limit and improves the fusion. We propose the use of a grid of particles to approximate the mean and covariance of the states.We have tested the algorithm by processing data from an Ultra Wide Band (UWB) positioning sensor attached to an IMU, both placed on the helmet of a person, and a foot-mounted IMU. Our results show that the system is able to estimate the position of a person with a limited error growth for the dead reckoning system and a better position estimate between position updates for the UWB system.

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“…For footmounted sensors, zero velocity update (ZUPT) calibration is exploited to adjust the positioning parameters by detecting stance phases within the gait cycle (static phase), though this calibration is not possible with handheld devices because of free hand motion and an increased difficulty to detect the stance phase. Outdoors, PDR can still be aided by GNSS [1], but this is not feasible indoors because of signal unavailability and further measurements are needed. These could be provided by radio beacons or visual information.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Dead Reckoning Navigation with the Help of A⁎-Based Routing Graphs in Large Unconstrained Spaces

Alaoui¹,

Betaille²,

Renaudin³

2017

Wireless Communications and Mobile Computing

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An A* -based routing graph is proposed to assist PDR indoor and outdoor navigation with handheld devices. Measurements are provided by inertial and magnetic sensors together with a GNSS receiver. The novelty of this work lies in providing a realistic motion support that mitigates the absence of obstacles and enables the calibration of the PDR model even in large spaces where GNSS signal is unavailable. This motion support is exploited for both predicting positions and updating them using a particle filter. The navigation network is used to correct for the gyro drift, to adjust the step length model and to assess heading misalignment between the pedestrian's walking direction and the pointing direction of the handheld device. Several datasets have been tested and results show that the proposed model ensures a seamless transition between outdoor and indoor environments and improves the positioning accuracy. The drift is almost cancelled thanks to heading correction in contrast with a drift of 8% for the nonaided PDR approach. The mean error of filtered positions ranges from 3 to 5 m.

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Data Fusion Algorithms for Multiple Inertial Measurement Units

Cited by 110 publications

References 17 publications

Optimal Configuration of Redundant Inertial Sensors Considering Lever Arm Effect

Optimal Configuration of Redundant Inertial Sensors Considering Lever Arm Effect

A constraint approach for UWB and PDR fusion

Pedestrian Dead Reckoning Navigation with the Help of A⁎-Based Routing Graphs in Large Unconstrained Spaces

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