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Kalman Filter Based Spatial Prediction of Wireless Connectivity for Autonomous Robots and Connected Vehicles
Purdue Univ, W Lafayette, IN 47906 USA..
KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.ORCID-id: 0000-0002-7714-928X
Purdue Univ, W Lafayette, IN 47906 USA..
2018 (Engelska)Ingår i: 2018 IEEE 88TH VEHICULAR TECHNOLOGY CONFERENCE (VTC-FALL), IEEE , 2018Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This paper proposes a new Kalman filter based online framework to estimate the spatial wireless connectivity in terms of received signal strength (RSS), which is composed of path loss and the shadow fading variance of a wireless channel in autonomous vehicles. The path loss is estimated using a localized least squares method and the shadowing effect is predicted with an empirical (exponential) variogram. A discrete Kalman Filter is used to fuse these two models into a state space formulation. The approach is unique in a sense that it is online and does not require the exact source location to be known apriori. We evaluated the method using real-world measurements dataset from both indoors and outdoor environments. The results show significant performance improvements compared to state-of-the-art methods using Gaussian processes or Kriging interpolation algorithms. We are able to achieve a mean prediction accuracy of up to 96% for predicting RSS as far as 20 meters ahead in the robot's trajectory.

Ort, förlag, år, upplaga, sidor
IEEE , 2018.
Serie
IEEE Vehicular Technology Conference Proceedings, ISSN 1550-2252
Nationell ämneskategori
Robotteknik och automation
Identifikatorer
URN: urn:nbn:se:kth:diva-255256DOI: 10.1109/VTCFall.2018.8690611ISI: 000468872400064Scopus ID: 2-s2.0-85064954564ISBN: 978-1-5386-6358-5 (tryckt)OAI: oai:DiVA.org:kth-255256DiVA, id: diva2:1339392
Konferens
88th IEEE Vehicular Technology Conference (VTC-Fall), AUG 27-30, 2018, Chicago, IL
Anmärkning

QC 20190729

Tillgänglig från: 2019-07-29 Skapad: 2019-07-29 Senast uppdaterad: 2019-07-29Bibliografiskt granskad

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Ögren, Petter
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Robotik, perception och lärande, RPL
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