A Wireless Sensor Network for Real-Time Indoor Localisation and Motion Monitoring

Klingbeil, Lasse; Wark, Tim

doi:10.1109/ipsn.2008.15

Cited by 119 publications

(104 citation statements)

References 24 publications

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“…where r2 = (R−L) and corresponding number of points denoted N 2 is the area of C A,r2 = π (r2) 2 . Putting the lower bound of the desirable range R = L√5/2 of an NTL, we get…”

Section: Regionmentioning

confidence: 99%

Performance Analysis Of An Improved Graded Precision Localization Algorithm For Wireless Sensor Networks

Sarangi¹,

Kar²

2010

IJCNC

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In this paper an improved version of the graded precision localization algorithm GRADELOC to allow for routing through them. Routing is not addressed by GRADELOC, but is of prime importance to the deployment of any adhoc network, especially sensor networks. A theoretical model expressing the radio range in terms of the cell dimensions of the grid infrastructure is proposed, to help in carrying out a deployment plan which achieves the desirable precision of coarse-grained localization. Secondly, in GRADELOC it is observedthat fine-grained localization does not achieve significant performance benefits over coarse-grained localization. In IGRADELOC, this factor is addressed with the introduction of a parameter that could be used to improve and fine-tune the precision of fine-grained localization..

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“…where r2 = (R−L) and corresponding number of points denoted N 2 is the area of C A,r2 = π (r2) 2 . Putting the lower bound of the desirable range R = L√5/2 of an NTL, we get…”

Section: Regionmentioning

confidence: 99%

Performance Analysis Of An Improved Graded Precision Localization Algorithm For Wireless Sensor Networks

Sarangi¹,

Kar²

2010

IJCNC

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“…Error analysis on the velocity estimation in (9) suggests that the geometry and the angle between a radar and the target also affect the estimation error of a target velocity. For simplicity, we assume the scenario in which we do not need to consider the geometry-when radar placements are equiangular.…”

Section: B Comparison Between Newton-raphson and Ekfmentioning

confidence: 99%

“…Nevertheless, most approaches proposed in the research literature either assume that the intended target has a device that actively transmits beacons (e.g., [9], [20]) or that the target naturally generates signals that can be detected (e.g., [1]). Nevertheless, there exist scenarios (e.g., intruder detection) in which one needs to detect targets that do not cooperate by intentionally or unintentionally emitting signals.…”

Section: Introductionmentioning

confidence: 99%

Tracking a non-cooperative mobile target using low-power pulsed Doppler radars

Lim

Terzis

Wang

2010

IEEE Local Computer Network Conference

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Abstract-Most target tracking applications developed for wireless sensor networks thus far employ passive sensors which detect the target's emissions. Their performance is thereby dictated by the magnitude of the target's signal and the receivers' sensitivity. Instead, we propose using a network of low-power Doppler radars that actively measure the target's radial velocity. Nodes combine their measurements to solve a system of nonlinear equations that estimate the target's position and velocity. Because these equations have no closed-form solutions we solve them using numerical methods. These methods however can lead to local minima or even diverge in the presence of even small measurement noise. For this reason we couple the proposed numerical method with an Extended Kalman filter that models the target's movement along a straight line. Clearly the target does not always follow a linear path and so we augment this simple Kalman filter with a method that detects the target's turns and updates the filter's dynamical model accordingly. The combination of the two approaches improve tracking accuracy compared to the numerical solution alone. Results from simulations and a prototype implementation suggest that the combined solution can effectively track a mobile target with average localization error as low as 25 cm in a 10×10 m outdoor field.

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“…The system is based on custom developed sensor network platform called Fleck [2]. Every Fleck contains a radio transmitter and a micro-controller as well as an interface to connect various sensor boards.…”

Section: System Descriptionmentioning

confidence: 99%

Demonstration of a Wireless Sensor Network for Real-Time Indoor Localisation and Motion Monitoring

Klingbeil

Wark

2008

2008 International Conference on Information Processing in Sensor Networks (Ipsn 2008)

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A Wireless Sensor Network for Real-Time Indoor Localisation and Motion Monitoring

Cited by 119 publications

References 24 publications

Performance Analysis Of An Improved Graded Precision Localization Algorithm For Wireless Sensor Networks

Performance Analysis Of An Improved Graded Precision Localization Algorithm For Wireless Sensor Networks

Tracking a non-cooperative mobile target using low-power pulsed Doppler radars

Demonstration of a Wireless Sensor Network for Real-Time Indoor Localisation and Motion Monitoring

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