Further results on the observability in magneto-inertial navigation

Batista, Pedro; Petit, Nicolas; Silvestre, Carlos; Oliveira, Paulo

doi:10.1109/acc.2013.6580210

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…Denoting P − and P + state-covariance matrices respectively before and after update, with Q u and R k chosen so as to represent known or hypothesized uncertainties about sensor measurements 2 , while Q is a covariance matrix modeling uncertainties about the uncertain discrete-time model itself 3 , we have:…”

Section: Ekf Implementationmentioning

confidence: 99%

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Motion estimation of a rigid body with an EKF using magneto-inertial measurements

Chesneau¹,

Hillion²,

Prieur³

2016

2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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International audienceThis paper studies the attitude and position estimation problem of a rigid body equipped with inertial and magnetic sensors. First a continuous-time model is provided linking the motion of the body and sensor measurements, which depend on the total magnetic field and measurement noise. The magnetic field may depend both on the space variable (since the indoor navigation is considered) and on the time variable (since power-line interference is taken into account). An Extended Kalman Filter is designed aiming at the minimization of the drifting error on the estimated position. A numerical simulation illustrates the motion estimation result. Some indoor tests are finally performed to confirm the approach and the obtained convergence on real experiments

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Section: Ekf Implementationmentioning

confidence: 99%

“…It was shown that the technique had potential applications in heavy launchers and space vehicles [19]. Finally, [2] studied observability issues that come with this technique.…”

Section: Introductionmentioning

confidence: 99%

Motion estimation of a rigid body with an EKF using magneto-inertial measurements

Chesneau¹,

Hillion²,

Prieur³

2016

2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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“…Generally, there are, in general, two categories of techniques used to enhance low-cost INS /GNSS integrated navigation performance during GNSS outage. The first one is adopting auxiliary sensors, such as a camera [10][11][12][13], Lidar [14,15], magnetic sensor [16], or odometer [17,18], to collect various information to aid INS. The drawbacks of this kind of solution include increases in hardware cost, equipment volume, and power consumption.…”

Section: Introductionmentioning

confidence: 99%

A GRU and AKF-Based Hybrid Algorithm for Improving INS/GNSS Navigation Accuracy during GNSS Outage

et al. 2022

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The integrated navigation system consisting of an inertial navigation system (INS) and Global Navigation Satellite System (GNSS) provides continuous high-accuracy positioning whereas the navigation accuracy during a GNSS outage inevitably degrades owing to INS error divergence. To reduce such degradation, a gated recurrent unit (GRU) and adaptive Kalman filter (AKF)-based hybrid algorithm is proposed. The GRU network, which has advantages of high accuracy and efficiency, is constructed to predict the position variations during GNSS outage. Furthermore, this paper takes the GRU-predicted error accumulation into consideration, and introduces AKF as a supplementary methodology to improve the navigation performance. The proposed hybrid algorithm is trained and tested by practical road datasets and compared with four algorithms, including the standard KF, Multi-Layer Perceptron (MLP)-aided KF, Long Short Time Memory (LSTM) aided KF, and GRU-aided KF. Periods of 180 and 120 s GNSS outage are employed to test the performance of the proposed algorithm in different time scales. The comparison result between the standard KF and neural network-aided KF indicates that the neural network is an effective methodology for bridging GNSS outages. The performance comparison between three kinds of neural networks demonstrate that both recurrent neural networks surpass the MLP in prediction position variation, and the GRU transcends the LSTM in prediction accuracy and training efficiency. Furthermore, it is concluded that the adaptive estimation theory is an effective complement to neural network-aided navigation, as the GRU-aided AKF reduced the horizontal error of GRU-aided KF by 31.71% and 16.12% after 180 and 120s of GNSS outage, respectively.

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“…An approach that only relies on a 3-axis magnetometer’s array, a 3-axis accelerometer and a 3-axis gyroscope, in the purpose of velocity estimation, was first introduced in [ 20 , 21 ], and further investigated in [ 22 , 23 , 24 ]. The proposed technique takes advantage of the magnetic field disturbances that are observed indoors, to estimate the inertial velocity.…”

Section: Introductionmentioning

confidence: 99%

Magnetic Field Gradient-Based EKF for Velocity Estimation in Indoor Navigation

Zmitri

Fourati

Prieur

2020

Sensors

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This paper proposes an advanced solution to improve the inertial velocity estimation of a rigid body, for indoor navigation, through implementing a magnetic field gradient-based Extended Kalman Filter (EKF). The proposed estimation scheme considers a set of data from a triad of inertial sensors (accelerometer and gyroscope), as well as a determined arrangement of magnetometers array. The inputs for the estimation scheme are the spatial derivatives of the magnetic field, from the magnetometers array, and the attitude, from the inertial sensors. As shown in the literature, there is a strong relation between the velocity and the measured magnetic field gradient. However, the latter usually suffers from high noises. Then, the novelty of the proposed EKF is to develop a specific equation to describe the dynamics of the magnetic field gradient. This contribution helps to filter, first, the magnetic field and its gradient and second, to better estimate the inertial velocity. Some numerical simulations that are based on an open source database show the targeted improvements. At the end of the paper, this approach is extended to position estimation in the case of a foot-mounted application and the results are very promising.

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Further results on the observability in magneto-inertial navigation

Cited by 5 publications

References 10 publications

Motion estimation of a rigid body with an EKF using magneto-inertial measurements

Motion estimation of a rigid body with an EKF using magneto-inertial measurements

A GRU and AKF-Based Hybrid Algorithm for Improving INS/GNSS Navigation Accuracy during GNSS Outage

Magnetic Field Gradient-Based EKF for Velocity Estimation in Indoor Navigation

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