Robotics-based location sensing using wireless ethernet
Proceedings of the 8th annual international conference on Mobile computing and networking
A Statistical Modeling Approach to Location Estimation
IEEE Transactions on Mobile Computing
WLAN Location Determination via Clustering and Probability Distributions
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
LANDMARC: Indoor Location Sensing Using Active RFID
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Employing User Feedback for Fast, Accurate, Low-Maintenance Geolocationing
PERCOM '04 Proceedings of the Second IEEE International Conference on Pervasive Computing and Communications (PerCom'04)
Adaptive Temporal Radio Maps for Indoor Location Estimation
PERCOM '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications
Reducing the Calibration Effort for Location Estimation Using Unlabeled Samples
PERCOM '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications
Statistical learning theory for location fingerprinting in wireless LANs
Computer Networks and ISDN Systems
Learning and inferring transportation routines
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Policy recognition in the abstract hidden Markov model
Journal of Artificial Intelligence Research
Location sensing and privacy in a context-aware computing environment
IEEE Wireless Communications
IEEE Transactions on Knowledge and Data Engineering
Kernel-Based Positioning in Wireless Local Area Networks
IEEE Transactions on Mobile Computing
Mining globally interesting patterns from multiple databases using kernel estimation
Expert Systems with Applications: An International Journal
Online co-localization in indoor wireless networks by dimension reduction
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Co-localization from labeled and unlabeled data using graph Laplacian
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
| Hi-index | 0.00 |
We present a novel method for indoor-location estimation using a vector-space model based on signals received from a wireless client. Our aim is to obtain an accurate mapping between the signal space and the physical space without incurring too much human calibration effort. This problem has traditionally been tackled through probabilistic models trained on manually labeled data, which are expensive to obtain. In this paper, we present a novel approach to building a mapping between the signalvector space and the physical location space using kernel canonical correlation analysis (KCCA). Its training requires much less human labor. Moreover, unlike traditional location-estimation systems that treat grid points as independent and discrete target classes during training, we use the physical location as a continuous feedback to build a similarity mapping using KCCA. We test our algorithm in a 802.11 wireless LAN environment, and demonstrate the advantage of our method in both accuracy and its ability to utilize a much smaller set of labeled training data than previous methods.