Data processing algorithms for MEMS based multi-component inertial measurement unit for indoor navigation

Mikov, Aleksandr; Galov, Aleksandr

doi:10.23919/icins.2017.7995564

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…Using ( 6) and (7), it is possible to change the current set of variables to x n = [p n v n β n ] T and to consider the linear system with matrix F n :…”

Section: B Linearized Systemmentioning

confidence: 99%

“…Since a new measurement Both the position and the orientation of the devices can also be corrected if there are data from two different IMUs fixed respectively on the right and left legs. The advantage of using multiple sensors has been shown, for example, in reference [7]. In this work, we use the additional assumption that positions of the sensors in space cannot significantly differ from each other.…”

Section: Introductionmentioning

confidence: 99%

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Pedestrian dead-reckoning algorithms for dual foot-mounted inertial sensors

Chistiakov¹,

Никулин²,

Gartseev³

2019

2019 26th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS)

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This work proposes algorithms for reconstruction of closed-loop pedestrian trajectories based on two foot-mounted inertial measurement units (IMU). The first proposed algorithm allows calculation of a trajectory using measurements from only one IMU. The second algorithm uses data 1 from both footmounted IMUs simultaneously. Both algorithms are based on the Kalman filter and the assumption that while a foot is on the ground its velocity is supposed to be zero. Two methods for comparing the obtained trajectories are proposed, advantages and disadvantages of each method are indicated and a way to optimize the computation time is presented. In addition, a method is proposed for constructing one generalized trajectory of human motion based on the trajectories of each leg.

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“…Using ( 6) and (7), it is possible to change the current set of variables to x n = [p n v n β n ] T and to consider the linear system with matrix F n :…”

Section: B Linearized Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%