Inertial Navigation System/Global Positioning System Fusion Algorithm Design in a Fast Prototyping Environment: Towards a Real-Time Implementation

Giroux, Richard; Gourdeau, Richard; Landry, René

doi:10.5589/q05-012

Cited by 1 publication

(3 citation statements)

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“…If the errors of the inertial system remain small, the linear dynamics model in the Kalman filter is acceptable. But if the low cost inertial sensors are used in the system, the estimated errors by the Kalman filter will drift with time [9]. The feedback structure removes the deficiency of the feedforward structure (Fig.3).…”

Section: Kalman Filter Implementationmentioning

confidence: 99%

“…The linearized equations related to the velocity and orientation errors may be expressed as follows: (9) Where ψ is the orientation error vector. δα and δβ are the tilt errors and δγ is the heading error.…”

Section: Sins Error Equationsmentioning

confidence: 99%

“…Eventually, the depth error may be expressed by the following equation: (11) Where δx is the error state vector of the system may be included the position, velocity, orientation, F is the system error matrix and is derived from the Eqs. (8), (9), and (10). L is a dynamic noise distribution matrix, random process w is a white-noise vector with zero mean and a power spectral density Q c .…”

Section: Sins Error Equationsmentioning

confidence: 99%

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Implementation and performance comparison of indirect Kalman filtering approaches for AUV integrated navigation system using low cost IMU

Sheijani

Davari

Emami

2013

2013 21st Iranian Conference on Electrical Engineering (ICEE)

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Strapdown Inertial Navigation System (SINS) estimates position, velocity, and attitude of vehicle using the signals measured by accelerometer and gyroscope and is based on dead-reckoning principle. Due to different imperfections in measurements, and the consecutive integration of the acceleration signals, estimation error increases with time and it is acceptable only for short times in the low cost SINS. In order to reduce the error, auxiliary sensors together with inertial sensors are utilized and to combine the data estimated by SINS with the signals measured by auxiliary sensors, data fusion methods based on direct and indirect Kalman filtering is used. The feedforward and feedback structures are two common approaches of the indirect filtering. In this paper, an underwater integrated navigation system has been designed using indirect filtering approaches due to lower computational load and higher reliability with respect to the direct approach. The auxiliary sensors used consist of DVL, Gyrocompass, and depthmeter. The performance of the designed system has been studied using real measurements. The experimental results showed that the root mean square error in the estimated position for feedforward structure is reduced from 3.2 to 0.2 percent of the travelled distance when using feedback structure.

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Section: Kalman Filter Implementationmentioning

confidence: 99%

Section: Sins Error Equationsmentioning

confidence: 99%

Section: Sins Error Equationsmentioning

confidence: 99%

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