The Development of Airborne Dead Reckoning. Part II: After 1940 – Staying On Track

Ayliffe, Alec

doi:10.1017/s0373463301001461

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…Reference [1] explains its use in ship navigation in detail. It has been used in air navigation since the beginning of 1900s; a thorough survey appears in [2,3]. A survey on the positioning and navigation methods for vehicles appears in [4].…”

Section: Introductionmentioning

confidence: 99%

Pedestrian dead reckoning employing simultaneous activity recognition cues

Altun

Barshan

2012

Meas. Sci. Technol.

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We consider the human localization problem using body-worn inertial/magnetic sensor units. Inertial sensors are characterized by a drift error caused by the integration of their rate output to obtain position information. Because of this drift, the position and orientation data obtained from inertial sensors are reliable over only short periods of time. Therefore, position updates from externally referenced sensors are essential. However, if the map of the environment is known, the activity context of the user can provide information about his position. In particular, the switches in the activity context correspond to discrete locations on the map. By performing localization simultaneously with activity recognition, we detect the activity context switches and use the corresponding position information as position updates in a localization filter. The localization filter also involves a smoother that combines the two estimates obtained by running the zero-velocity update algorithm both forward and backward in time. We performed experiments with eight subjects in indoor and outdoor environments involving walking, turning and standing activities. Using a spatial error criterion, we show that the position errors can be decreased by about 85% on the average. We also present the results of two 3D experiments performed in realistic indoor environments and demonstrate that it is possible to achieve over 90% error reduction in position by performing localization simultaneously with activity recognition.

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