Non-GPS Navigation for Security Personnel and First Responders

Ojeda, Lauro

doi:10.1017/s0373463307004286

Cited by 242 publications

(224 citation statements)

References 13 publications

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“…Skog et al (2010) developed a novel stance hypothesis optimal detector; however this is restricted to 2D cases. Ojeda and Borenstein (2007) implemented two empiric rules for gyro signals analysis to detect zero velocity. The system was tested with a variety of walking patterns, on stairs and on rugged terrain.…”

Section: Introductionmentioning

confidence: 99%

A Pedestrian Navigation System Based on Low Cost IMU

Wang

2014

J. Navigation

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For indoor pedestrian navigation with a shoe-mounted inertial measurement unit (IMU), the zero velocity update (ZUPT) technique is implemented to constrain the sensors' error. ZUPT uses the fact that a stance phase appears in each step at zero velocity to correct IMU errors periodically. This paper introduces three main contributions we have achieved based on ZUPT. Since correct stance phase detection is critical for the success of applying ZUPT, we have developed a new approach to detect the stance phase of different gait styles, including walking, running and stair climbing. As the extension of ZUPT, we have proposed a new concept called constant velocity update (CUPT) to correct IMU errors on a moving platform with constant velocity, such as elevators or escalators where ZUPT is infeasible. A closedloop step-wise smoothing algorithm has also been developed to eliminate discontinuities in the trajectory caused by sharp corrections. Experimental results demonstrate the effectiveness of the proposed algorithms. K E Y

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Section: Introductionmentioning

confidence: 99%

A Pedestrian Navigation System Based on Low Cost IMU

Wang

2014

J. Navigation

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“…The step length is time-varying, and can be estimated online using its linear relationship with step frequency [7], while the heading is obtained from the data measured by gyroscope or magnetometer. The accuracy of navigation system based on PDR alone can be as low as 2%-10%, where acceleration can be measured on the torso, waist, foot and head [8][9][10][11]; however the error may grow quickly due to the inherent instrumental error, disturbances from the environment and unsteady walking, in addition to error of step length calculation and increment of the heading error as a non-observable state [12,13].…”

Section: Introductionmentioning

confidence: 99%

Maryam Banitalebi DePedestrian Indoor Navigation System Using Inertial Measurement Unit

Dehkordi¹,

Frisoli²,

Sotgiu³

et al. 2014

Sensor Netw Data Commun

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This paper presents a method for an indoor pedestrian localization, based on the data that solely are measured by a foot-mounted Inertial Measurement Unit (IMU). To locate the user accurately, a comprehensive Extended Kalman Filter (EKF) with five states is developed. Five different error reduction methods are employed to estimate the errors of all five states. These error reduction methods feed EKF independently, at stance phases or different time intervals of swing phases. The navigation system is developed using the accelerometer and gyroscope measurements and without magnetometer, thus it is insensitive to the presence of metal and magnetic fields, and it is able to estimate the user's tracked trajectory with the same accuracy in both indoor and outdoor environments. The system does not rely on the measurement from external infrastructure (e.g., RFID). To evaluate the accuracy of the system, several experimental tests are carried out over the known trajectories. Results demonstrate that the error of the estimated tracked trajectory is less than 1% of the total traveled distance.

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“…Some methods are based on step counters and estimate the distance covered using an average step length [1], [2], [3]. Other methods are based on doubly integrating the acceleration measurements during the walking movement [4], [5], [6]. Yet others try to determine the direction in which the pedestrian is walking to estimate a trajectory.…”

mentioning

confidence: 99%

Pedestrian tracking using inertial sensors

Alonso¹,

Zalama²,

Gómez-García-Bermejo³

2009

jopha

100

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Abstract-A personal navigation system is proposed in the present paper. This system is based on the use of inertial sensors and will be a complement for the Global Positioning System in places where GPS signals are not available. Such places are indoors and outdoors without a clear visibility of the sky (urban canyons, places under dense foliage…).A known initial position is required for this method for it to be able to estimate the trajectory of the pedestrian using inertial sensors attached to the foot.The developed method and the approximations taken into account are explained. Finally, the results of several tests carried out are shown.

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Non-GPS Navigation for Security Personnel and First Responders

Cited by 242 publications

References 13 publications

A Pedestrian Navigation System Based on Low Cost IMU

A Pedestrian Navigation System Based on Low Cost IMU

Maryam Banitalebi DePedestrian Indoor Navigation System Using Inertial Measurement Unit

Pedestrian tracking using inertial sensors

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