Analysis of attitude determination algorithm TRIAD errors

Ryzhkov, L. М.; Ozhoh, M.V.; Prokopovych, O.V.

doi:10.1109/msnmc.2014.6979763

Cited by 2 publications

(3 citation statements)

References 2 publications

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“…This happens because the TRIAD algorithm relies only on the measurements of the accelerometer and the magnetometer at time k and at the beginning of the experiment. Consequently, the error of the TRIAD algorithm is related only to the momentarily measurement error of the two sensors at these particular time periods as shown in [31]. Therefore, during State 3, an accurate and independent attitude estimate is obtained.…”

Section: A Attitude Error Estimationmentioning

confidence: 99%

MAGINAV: Long-Term Accurate Navigation Algorithm Using Inertial and Magnetic Field Sensor Fusion

Papafotis,

Sotiriadis

2023

IEEE Access

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MAGINAV (MAGnetic Inertial NAVigation) algorithm provides accurate estimation of velocity, attitude and position in long-term. It is utilized in a specialized pedestrian navigation system that consists of a 3-axis accelerometer, a 3-axis gyroscope, and a 3-axis magnetometer mounted on the shoe of a walking human. MAGINAV compensates for the attitude error, accumulated over time by utilizing a second attitude estimation obtained by fusing the measurements from the accelerometer and the magnetometer Instead of employing a complex Attitude Heading Reference System (AHRS), MAGINAV utilizes the computationally efficient TRIAD algorithm, alongside two popular algorithms for zero-velocity detection and magneticdisturbance detection respectively.The proposed algorithm undergoes testing in an outdoor environment utilizing low-cost commercial inertial and magnetic field sensors. Remarkably, it achieves exceptional long-term accuracy, with a position error that is less than 0.25% of the total distance in a 20-minute walk spanning 1.3km.

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Section: A Attitude Error Estimationmentioning

confidence: 99%

MAGINAV: Long-Term Accurate Navigation Algorithm Using Inertial and Magnetic Field Sensor Fusion

Papafotis,

Sotiriadis

2023

IEEE Access

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“…When the noise of the major vector is bigger than the secondary vector, then the classic TRIAD is not the optimized solution. In [9] the error model of TRIAD was given as: ( where TRIAD P is the attitude covariance matrix. I is an identity matrix.…”

Section: Classic Triadmentioning

confidence: 99%

“…According to the error matrix of TRIAD derived in [8], the accuracy of attitude determination was dominated by the wideband noise of vector observations. Later in [9] it has been illustrated that different order of reference vectors affected the results, because of weighting too much on the first vector. The two vectors were distinguished by the order.…”

Section: Introductionmentioning

confidence: 99%