Robust in‐field estimation and calibration approach for strapdown inertial navigation systems accelerometers bias acting on the vertical channel

Rahgoshay, Mohammad Ali; Karimaghaie, Paknoosh

doi:10.1049/iet-rsn.2019.0359

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…Kalman Filter (KF) is the most widely used optimal state estimator in many theorical and industrial applications including integrated navigation systems (6)(7)(8)(9)(10)(11). To achieve an optimal solution in the KF, determining proper models for the system and stochastic noises is a key factor problem (12). In the integrated navigation system, the system model is mostly deterministic and does not raise a serious issue in the KF-based estimator.…”

Section: Introductionmentioning

confidence: 99%

A Novel Sensor Integration Scheme for an Aided Inertial Navigation System Based on a Generalized PID Filter in the Presence of Observation Uncertainty

Ansari,

Jamali,

Fatehi-Dindarlou

et al. 2024

IJE

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Implementing a proper integration scheme plays an important role in the performance of integrated navigation systems. Not only does employing a more reliable estimation method improve the accuracy of the integrated navigation system, but this can lead to a more robust solution in the presence of different types of uncertainties. Implementing an integration scheme that has a robust and simple structure is a challenging issue in the design of integrated navigation systems. By inspiring from the concept of PID control, this paper proposes a robust integration scheme for aided inertial navigation systems in the presence of aiding sensor measurement uncertainties. The proposed filter combines the concept of proportional-integral-derivative control theory and the standard Kalman filter estimator to improve the performance of the integration scheme. Thanks to the integral and derivative parts added to the proposed scheme, the integrated system attains a faster and more robust solution in the presence of observation errors and uncertainties. The simulation case studies validate the superior efficacy and capability of the proposed scheme compared to the integration method based on the standard Kalman filter.

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Section: Introductionmentioning

confidence: 99%

A Novel Sensor Integration Scheme for an Aided Inertial Navigation System Based on a Generalized PID Filter in the Presence of Observation Uncertainty

Ansari,

Jamali,

Fatehi-Dindarlou

et al. 2024

IJE

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“…The main task of strapdown inertial navigation system (SINS) is to extract the attitude, velocity and position of the vehicle continuously [1]. In general, the SINS function is divided into initial alignment and mechanisation phases [2]. In the initial alignment stage, the orientation of the body frame is computed accurately with respect to the navigation frame.…”

Section: Introductionmentioning

confidence: 99%

Overcoming the settling time challenge of initial alignment of strapdown inertial navigation system by a novel solution

Jameian

Safarinejadian

2021

IET Radar Sonar & Navi

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This study investigates two fundamental challenges in the initial alignment of strapdown inertial navigation systems and presents creative solutions to overcome these challenges. The first challenge is to determine the settling time in different operating conditions such as stationary, quasistationary, in-motion, etc. The proposed solution to this challenge is to adjust the settling time of the initial alignment adaptively by evaluating the initial attitude matrix computed using the vector observations instead of allocating a fixed interval time for settling time. The second challenge is the existence of uncertainties in the vector observations, which adversely affect the initial alignment performance. The proposed solution to this challenge is to evaluate the quality of the vector observations used in the initial alignment by a real-time algorithm. Appropriate weights are assigned to vector observations instead of allocating a uniform weight to all vectors, depending on the vector observation's quality. The capability and performance of the proposed solutions are evaluated using different experimental scenarios such as three-axis rate table and vehicle tests. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Estimation of IMU orientation using Nesterov’s accelerated gradient improved by fuzzy control rule

Tang

Fan

et al. 2021

Sensors and Actuators A: Physical

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Robust in‐field estimation and calibration approach for strapdown inertial navigation systems accelerometers bias acting on the vertical channel

Cited by 3 publications

References 24 publications

A Novel Sensor Integration Scheme for an Aided Inertial Navigation System Based on a Generalized PID Filter in the Presence of Observation Uncertainty

A Novel Sensor Integration Scheme for an Aided Inertial Navigation System Based on a Generalized PID Filter in the Presence of Observation Uncertainty

Overcoming the settling time challenge of initial alignment of strapdown inertial navigation system by a novel solution

Estimation of IMU orientation using Nesterov’s accelerated gradient improved by fuzzy control rule

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