Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Dynamic fine-grained localization in Ad-Hoc networks of sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
LANDMARC: Indoor Location Sensing Using Active RFID
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
A spatio-temporal extension to Isomap nonlinear dimension reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A kernel-based learning approach to ad hoc sensor network localization
ACM Transactions on Sensor Networks (TOSN)
ICML '05 Proceedings of the 22nd international conference on Machine learning
Incremental Nonlinear Dimensionality Reduction by Manifold Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous localization, calibration, and tracking in an ad hoc sensor network
Proceedings of the 5th international conference on Information processing in sensor networks
Distributed localization of networked cameras
Proceedings of the 5th international conference on Information processing in sensor networks
IEEE Transactions on Knowledge and Data Engineering
Bayesian Filtering for Location Estimation
IEEE Pervasive Computing
A manifold regularization approach to calibration reduction for sensor-network based tracking
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Large-scale localization from wireless signal strength
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Co-localization from labeled and unlabeled data using graph Laplacian
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
WiFi-SLAM using Gaussian process latent variable models
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Accurate and low-cost location estimation using kernels
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Rapid and brief communication: Incremental locally linear embedding
Pattern Recognition
Real world activity recognition with multiple goals
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
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This paper addresses the problem of recovering the locations of both mobile devices and access points from radio signals that come in a stream manner, a problem which we call online co-localization, by exploiting both labeled and unlabeled data from mobile devices and access points. Many tracking systems function in two phases: an offline training phase and an online localization phase. In the training phase, models are built from a batch of data that are collected offline. Many of them can not cope with a dynamic environment in which calibration data may come sequentially. In such case, these systems may gradually become inaccurate without a manually costly re-calibration. To solve this problem, we proposed an online co-localization method that can deal with labeled and unlabeled data stream based on semi-supervised manifold-learning techniques. Experiments conducted in wireless local area networks show that we can achieve high accuracy with less calibration effort as compared to several previous systems. Furthermore, our method can deal with online stream data relatively faster than its two-phase counterpart.