Inertial Navigation System

Noureldin, Aboelmagd; Karamat, Tashfeen B.; Georgy, Jacques

doi:10.1007/978-3-642-30466-8_4

Cited by 27 publications

(38 citation statements)

References 4 publications

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“…The dynamic model is non-linear and can be represented in discrete form as follows [ 17 , 18 ]:

where

is the dynamic model,

is the stochastic model, and

is the process noise.…”

Section: Methodsmentioning

confidence: 99%

A Novel Method to Enhance Pipeline Trajectory Determination Using Pipeline Junctions

Sahli

El‐Sheimy

2016

Sensors

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Pipeline inspection gauges (pigs) have been used for many years to perform various maintenance operations in oil and gas pipelines. Different pipeline parameters can be inspected during the pig journey. Although pigs use many sensors to detect the required pipeline parameters, matching these data with the corresponding pipeline location is considered a very important parameter. High-end, tactical-grade inertial measurement units (IMUs) are used in pigging applications to locate the detected problems of pipeline using other sensors, and to reconstruct the trajectories of the pig. These IMUs are accurate; however, their high cost and large sizes limit their use in small diameter pipelines (8″ or less). This paper describes a new methodology for the use of MEMS-based IMUs using an extended Kalman filter (EKF) and the pipeline junctions to increase the position parameters’ accuracy and to reduce the total RMS errors even during the unavailability of above ground markers (AGMs). The results of this new proposed method using a micro-electro-mechanical systems (MEMS)-based IMU revealed that the position RMS errors were reduced by approximately 85% compared to the standard EKF solution. Therefore, this approach will enable the mapping of small diameter pipelines, which was not possible before.

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“…The dynamic model is non-linear and can be represented in discrete form as follows [ 17 , 18 ]:

where

is the dynamic model,

is the stochastic model, and

is the process noise.…”

Section: Methodsmentioning

confidence: 99%

A Novel Method to Enhance Pipeline Trajectory Determination Using Pipeline Junctions

Sahli

El‐Sheimy

2016

Sensors

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“…INS apply the dead reckoning principle where the actual position of a vehicle is obtained from the knowledge of the previous position and the measurements of accelerations and angular rotations. The velocity is obtained using initial conditions and one integration of acceleration; the position is obtained after a second integration, and finally the altitude is given by processing angular rates [2]. An INS is composed by three main modules: inertial measurement unit (IMU), signal pre-processing unit and a mechanization unit (Fig.…”

Section: Ins and Fdsmentioning

confidence: 99%

Acceleration measurement improvement by application of novel frequency measurement technique for FDS based INS

Murrieta-Rico

Hernández-Balbuena

Petranovskii

et al. 2014

2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE)

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Inertial navigation systems have as sensing elements gyroscopes and accelerometers. The accelerometers with a frequency output domain have some outstanding characteristics like output of quasi-digital signals, high sensitivity, high resolution, wide dynamic range, anti-interference capacity and good stability. This work explains how this sensor works inside an inertial navigation unit and how the measurement of acceleration can be improved through a novel frequency measurement principle. Also it is examined how in a common accelerometer the frequency measurement by principle of rational approximation can be easily implemented with low cost-affordable components. The error analysis in the measurement process is introduced.

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“…When calculating the azimuth angle, both the Earth’s rotation and the change of orientation of the local-level frame are taken into consideration [35]. Thus, the calculation of azimuth angle

A_{k}

is:

A_{k} = \tan^{- 1} \frac{U^{E}}{U^{N}} + (ω^{e} \sin φ_{k - 1}) Δ t + \frac{v_{k - 1}^{w h} \sin A_{k - 1} \cos p_{k - 1} \tan φ_{k - 1}}{R_{N} + h_{k - 1}} Δ t

where:

$U^{E} = \sin A_{k - 1} \cos p_{k - 1} \cos γ_{k}^{z} - (\cos A_{k - 1} \cos r_{k - 1} + \sin A_{k - 1} \sin p_{k - 1} \sin r_{k - 1}) \sin γ_{k}^{z}$

$U^{N} = \cos A_{k - 1} \cos p_{k - 1} \cos γ_{k}^{z} - (- \sin A_{k - 1} \cos r_{k - 1} + \cos A_{k - 1} \sin p_{k - 1} \sin r_{k - 1}) \sin γ_{k}^{z}$

$γ_{k}^{z} = ω_{k}^{z} Δ t$ , $ω^{e}$ is the Earth’s rotation rate,

$φ_{k}$ is the latitude of the vehicle position,

$h_{k}$ is the altitude,

$R_{N}$ is the normal radius of curvature of the Earth.

…”

Section: Proposed Imm-ukf Algorithmmentioning

confidence: 99%

A Cost-Effective Vehicle Localization Solution Using an Interacting Multiple Model−Unscented Kalman Filters (IMM-UKF) Algorithm and Grey Neural Network

Liu

Chan

2017

Sensors

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In this paper, we propose a cost-effective localization solution for land vehicles, which can simultaneously adapt to the uncertain noise of inertial sensors and bridge Global Positioning System (GPS) outages. First, three Unscented Kalman filters (UKFs) with different noise covariances are introduced into the framework of Interacting Multiple Model (IMM) algorithm to form the proposed IMM-based UKF, termed as IMM-UKF. The IMM algorithm can provide a soft switching among the three UKFs and therefore adapt to different noise characteristics. Further, two IMM-UKFs are executed in parallel when GPS is available. One fuses the information of low-cost GPS, in-vehicle sensors, and micro electromechanical system (MEMS)-based reduced inertial sensor systems (RISS), while the other fuses only in-vehicle sensors and MEMS-RISS. The differences between the state vectors of the two IMM-UKFs are considered as training data of a Grey Neural Network (GNN) module, which is known for its high prediction accuracy with a limited amount of samples. The GNN module can predict and compensate position errors when GPS signals are blocked. To verify the feasibility and effectiveness of the proposed solution, road-test experiments with various driving scenarios were performed. The experimental results indicate that the proposed solution outperforms all the compared methods.

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Inertial Navigation System

Cited by 27 publications

References 4 publications

A Novel Method to Enhance Pipeline Trajectory Determination Using Pipeline Junctions

A Novel Method to Enhance Pipeline Trajectory Determination Using Pipeline Junctions

Acceleration measurement improvement by application of novel frequency measurement technique for FDS based INS

A Cost-Effective Vehicle Localization Solution Using an Interacting Multiple Model−Unscented Kalman Filters (IMM-UKF) Algorithm and Grey Neural Network

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