Orthogonal vs Skewed Inertial Sensors Redundancy: A New Paradigm for Low-Cost Systems

Giroux, Richard

doi:10.1016/s1474-6670(17)32350-9

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…Since logarithm function is monotonic increasing function, taking the logarithm of the objective function does not change the optimal solution. Therefore, the problem in (10) becomes: minimize…”

Section: Problem Formulationmentioning

confidence: 99%

“…The criterion of minimum GDOP as FOM to analyze the cone configurations has also been used in [7], [8]. The idea of maximizing the determinant of the information matrix, which is the inverse of the estimation error covariance matrix, has been used in [9], [10] to determine the optimal configuration of any number of sensors. The author in [11] has proposed the partial redundancy method to determine the optimal configuration of multiple IMUs, which is based on the concept of reliability and is typically applied in the geodetic network.…”

Section: Introduction and Literaturementioning

confidence: 99%

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A Novel Algorithm for Optimal Placement of Multiple Inertial Sensors to Improve the Sensing Accuracy

Sahu

Babu

Kumar

et al. 2020

IEEE Trans. Signal Process.

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This paper proposes a novel algorithm to determine the optimal placement of redundant inertial sensors such as accelerometers and gyroscopes (gyros) for increasing the sensing accuracy. In this paper, we have proposed a novel iterative algorithm to find the optimal sensor configuration. The proposed algorithm utilizes the majorization-minimization (MM) algorithm and the duality principle to find the optimal configuration. Unlike the state-of-the-art which are mainly geometrical in nature and restricted to certain noise statistics, the proposed algorithm gives the exact positions of the sensors, and moreover, the proposed algorithm is independent of the nature of the noise at different sensors. The proposed alogrithm has been implemented and tested via numerical simulation in the MATLAB. The simulation results show that the algorithm converges to the optimal configurations and show the effectiveness of the proposed algorithm.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Introduction and Literaturementioning

confidence: 99%

A Novel Algorithm for Optimal Placement of Multiple Inertial Sensors to Improve the Sensing Accuracy

Sahu

Babu

Kumar

et al. 2020

IEEE Trans. Signal Process.

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show abstract

“…The trace of the estimation error covariance matrix has been used as the FOM to analyze and determine the optimal configuration of redundant sensors in [4][5][6]. The determinant of the information matrix defined as the inverse of the estimation error covariance matrix has been used as a criteria to put the sensors' axis in optimal direction for optimal sensing in [7,8]. The partial redundancy method which is typically used in the geodetic network has been used in [9] to study and determine the optimal configuration of the multiple inertial measurement units (IMUs).…”

Section: Introductionmentioning

confidence: 99%

Optimally adding additional inertial sensors to an initial configuration of sensors to maximize the sensing accuracy

Sahu¹,

Babu²,

Kumar³

et al. 2021

Meas. Sci. Technol.

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In this paper, we solve the problem of optimally adding additional inertial sensors to any known initial configuration of inertial sensors. The initial known configuration may be the optimal configuration or any random configuration of sensors. In this problem, we do not change the initial configuration of sensors, instead we determine the optimal orientations of the sensors which are to be added. We have posed this problem as an optimization problem and proposed a novel method to solve this optimization problem. The proposed method is based on the majorization-minimization procedure. The proposed method has been implemented and tested via numerical simulations in MATLAB. The simulation results show that the proposed method converges to the optimal solution and hence determines the optimal orientations for the sensors which are added to any known configuration of the sensors.

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A Survey of Inertial Sensor Fusion: Applications in sUAS Navigation and Data Collection

Givens

Coopmans

2019

2019 International Conference on Unmanned Aircraft Systems (ICUAS)

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Orthogonal vs Skewed Inertial Sensors Redundancy: A New Paradigm for Low-Cost Systems

Cited by 3 publications

References 4 publications

A Novel Algorithm for Optimal Placement of Multiple Inertial Sensors to Improve the Sensing Accuracy

A Novel Algorithm for Optimal Placement of Multiple Inertial Sensors to Improve the Sensing Accuracy

Optimally adding additional inertial sensors to an initial configuration of sensors to maximize the sensing accuracy

A Survey of Inertial Sensor Fusion: Applications in sUAS Navigation and Data Collection

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