Data Fusion Architectures for Orthogonal Redundant Inertial Measurement Units

Gagnon, Éric; Vachon, Alexandre; Beaudoin, Y.

doi:10.3390/s18061910

Cited by 3 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The EKF is a predictor based on the Markov chain, which assumes that the positioning target obeys physical principles. It has been widely used in sensor fusion and data filtering [ 41 , 42 , 43 ]. The positions of the applied EKF are shown in Figure 10 b.…”

Section: Tdoa Positioning Algorithmmentioning

confidence: 99%

Range-Extension Algorithms and Strategies for TDOA Ultra-Wideband Positioning System

Huang

Chen

Wei

et al. 2023

Sensors

View full text Add to dashboard Cite

The Internet of Things (IoT) for smart industry requires the surveillance and management of people and objects. The ultra-wideband positioning system is an attractive solution for achieving centimeter-level accuracy in target location. While many studies have focused on improving the accuracy of the anchor coverage range, it is important to note that in practical applications, positioning areas are often limited and obstructed by furniture, shelves, pillars, or walls, which can restrict the placement of anchors. Furthermore, some positioning regions are located beyond anchor coverage, and a single group with few anchors may not be able to cover all rooms and aisles on a floor due to non-line-of-sight errors causing severe positioning errors. In this work, we propose a dynamic-reference anchor time difference of arrival (TDOA) compensation algorithm to enhance accuracy beyond anchor coverage by eliminating local minima of the TDOA loss function near anchors. We designed a multidimensional and multigroup TDOA positioning system with the aim of broadening the coverage of indoor positioning and accommodating complex indoor environments. By employing an address-filter technique and group-switching process, tags can seamlessly move between groups with a high positioning rate, low latency, and high accuracy. We deployed the system in a medical center to locate and manage researchers with infectious medical waste, demonstrating its usefulness for practical healthcare institutions. Our proposed positioning system can thus facilitate precise and wide-range indoor and outdoor wireless localization.

show abstract

Section: Tdoa Positioning Algorithmmentioning

confidence: 99%

Range-Extension Algorithms and Strategies for TDOA Ultra-Wideband Positioning System

Huang

Chen

Wei

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

Data Fusion in Redundant Inertial Measurement Unit Using a Fruit-Fly-Optimized Weighted Least Squares Algorithm

et al. 2021

View full text Add to dashboard Cite

Innovative Modeling of IMU Arrays Under the Generic Multi-Sensor Integration Strategy

Brunson,

Wang,

2024

Sensors

View full text Add to dashboard Cite

This research proposes a novel modeling method for integrating IMU arrays into multi-sensor kinematic positioning/navigation systems. This method characterizes sensor errors (biases/scale factor errors) for each IMU in an IMU array, leveraging the novel Generic Multisensor Integration Strategy (GMIS) and the framework for comprehensive error analysis in Discrete Kalman filtering developed through the authors’ previous research. This work enables the time-varying estimation of all individual sensor errors for an IMU array, as well as rigorous fault detection and exclusion for outlying measurements from all constituent sensors. This research explores the feasibility of applying Variance Component Estimation (VCE) to IMU array data, using separate variance components to characterize the performance of each IMU’s gyroscopes and accelerometers. This analysis is only made possible by directly modeling IMU inertial measurements under the GMIS. A real land-vehicle kinematic dataset was used to demonstrate the proposed technique. The a posteriori positioning/attitude standard deviations were compared between multi-IMU and single IMU solutions, with the multi-IMU solution providing an average accuracy improvement of ca. 14–16% in the estimated position, 30% in the estimated roll and pitch, and 40% in the estimated heading. The results of this research demonstrate that IMUs in an array do not generally exhibit homogeneous behavior, even when using the same model of tactical-grade MEMS IMU. Furthermore, VCE was used to compare the performance of three IMU sensors, which is not possible under other IMU array data fusion techniques. This research lays the groundwork for the future evaluation of IMU array sensor configurations.

show abstract

Data Fusion Architectures for Orthogonal Redundant Inertial Measurement Units

Cited by 3 publications

References 24 publications

Range-Extension Algorithms and Strategies for TDOA Ultra-Wideband Positioning System

Range-Extension Algorithms and Strategies for TDOA Ultra-Wideband Positioning System

Data Fusion in Redundant Inertial Measurement Unit Using a Fruit-Fly-Optimized Weighted Least Squares Algorithm

Innovative Modeling of IMU Arrays Under the Generic Multi-Sensor Integration Strategy

Contact Info

Product

Resources

About