The anatomy of a context-aware application
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
VOR base stations for indoor 802.11 positioning
Proceedings of the 10th annual international conference on Mobile computing and networking
Bayesian localization in wireless networks using angle of arrival
Proceedings of the 3rd international conference on Embedded networked sensor systems
Localization for indoor wireless networks using minimum intersection areas of iso-RSS lines
LCN '07 Proceedings of the 32nd IEEE Conference on Local Computer Networks
Bayesian Filtering for Location Estimation
IEEE Pervasive Computing
Restarting particle filters: an approach to improve the performance of dynamic indoor localization
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
A survey of convergence results on particle filtering methods forpractitioners
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Particle filters for positioning, navigation, and tracking
IEEE Transactions on Signal Processing
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We are considering the problem of dynamic localization of human targets in an indoor environment, such as an office building, where GPS signals are not receivable. Previous work has shown that static localization is possible through the measurement of the wireless signal strengths. Dynamic localization (tracking) can be achieved by performing periodic static localizations and filling in the gaps through an appropriate filtering technique. We are using a sampling-importance-resampling particle filter which is a probabilistic reasoning technique for this purpose. In this paper we present approaches through which information about the environment (such as the floor plan of the building) and the target (such as the physical and social limitations of the human movement) can be incorporated in the prediction and weight update components of the particle filter. The particle filter requires information to be presented as conditional probability distributions, a format which presents both representational and computational efficiency challenges. In addition, the models need to consider both the a priori information and the operational details of the particle filter. Even technically correct models can reduce the accuracy of the localization, by inadvertently reducing the effective number of particles, which, in its turn, induces resampling errors. Through a series of experiments we show that the correct usage of a priori information can significantly improve the accuracy of dynamic localization.