• Aucun résultat trouvé

Evaluation of Tracking Algorithms using Heterogeneous Technologies

N/A
N/A
Protected

Academic year: 2021

Partager "Evaluation of Tracking Algorithms using Heterogeneous Technologies"

Copied!
1
0
0

Texte intégral

(1)

Evaluation of Tracking Algorithms Using Heterogeneous Technologies

Evaluation of Tracking Algorithms Using Heterogeneous Technologies

Francesco Sottile(1), Maurizio A. Spirito(1),

Pau Closas(2), Javier Arribas(2), Carles Fernández(2),

EU funded project:

Partners:

Pau Closas(2), Javier Arribas(2), Carles Fernández(2), Montse Nájar(3), Eva Lagunas (3),

Davide Dardari(4), Nicolò Decarli(4),

Partners:

Davide Dardari(4), Nicolò Decarli(4), Andrea Conti(5),Matteo Guerra(5)

(1)Istituto Superiore Mario Boella (ISMB), Italy. (1)Istituto Superiore Mario Boella (ISMB), Italy.

(2)Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Spain. (3)Universitat Politècnica de Catalunya (UPC), Spain.

Network of Excellence in (3)Universitat Politècnica de Catalunya (UPC), Spain.

(4)University of Bologna (CNIT-BO), Italy (5)University of Ferrara (CNIT-FE), Italy

Network of Excellence in Wireless COMmunications

Workpackage WPR.B: University of Ferrara (CNIT-FE), Italy Workpackage WPR.B:

“Localization and Positioning"

Environment, Mobile Target, Matlab GUI

Indoor Office Environment

Matlab GUI

Actual Trajectory ∆ ∆∆ ∆TposEst= 500 m sec ∆ ∆∆ ∆TPosEst = 500 m sec

CC2420 ZigBee

Mobile Target

Algorithms Tested

LEGO

ZigBee

(CC2430) 3-axis accelerometer(IMOTE2)

CC2420 ZigBee

Module

PulsOn220

Algorithms Tested

Using Hybrid Data:

ZigBee-RSS, UWB-dist, Acc.

∆ ∆∆ ∆T = 50 m sec LEGO ROBOT Path

PulsOn220

UWB Module

ZigBee-RSS, UWB-dist, Acc.

 EKF (Extended Kalman Filter)

∆ ∆∆

∆TRSS = 50 m sec

Path

Accel. Module

 EKF-BT (EKF Bias Tracking )  PF (Particle Filter)

UWB

IMOTE2 Sensor Board ITS400

Y

Accel. Module

 CRPF (Cost Reference PF)

 CKF (Cubature Kalman Filter)

∆ ∆∆

∆TAccel = 2 m sec

UWB

(PulsOn220 by Time Domain)

X Z

Max Speed: 0.44 m/sec

∆ ∆ ∆

∆TUWB = 500 m sec

Max Speed: 0.44 m/sec

3-axis accelerometer

LIS3L02DQ

Performance

Performance

Constant Speed, Reduced Set of Anchors

Variable Speed, Reduced Set of Anchors

Constant Speed, Reduced Set of Anchors

Variable Speed, Reduced Set of Anchors

GLOBECOM 2010, Miami, Florida, USA

GLOBECOM 2010, Miami, Florida, USA

Références

Documents relatifs

37,39 Such an approach achieved in-vivo for the right liver lobe of eight subjects a spatio-temporal 3D mean prediction accuracy of 2.4 (2.7) mm for a system latency of 150 (400)

We have motivated our choices of the different complexity measures and have modeled the output of the evaluation metrics using the Least Sum of Squares Regression model considering

In recent years the development of distributed fusion sys- tems for real-world services (tracking, surveillance...) has remarked the importance of good design choices for both

Abstract : The quality of the tracking is greatly enhanced by arobust motion estimation.The objective is to develop a target tracking algorithm of amoving object, especially

Sequential Monte Carlo Methods : particule filters Particle methods are based on the principle of Monte Carlo to estimate and predict online dynamic systems.. They enable to

This simulator is then used to evaluate 4 pose estimation algorithms: a Complementary Observer, a Gradient Descent Algorithm, an Extended Kalman Filter and a Linear

Vertical System Integration (VSI®) [5] is characterized by bonding and very high density vertical inter-chip wiring of stacked thinned device substrates (Si) with freely positioned

Hypothesis 3 : The magnitude of the policy inducement effect, or more generally, of an increasing size of the energy market, is stronger for technologies with high