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
A novel backtracking particle filter for pattern matching indoor localization
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
Bayesian Filtering for Location Estimation
IEEE Pervasive Computing
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
Using a-priori information to improve the accuracy of indoor dynamic localization
Proceedings of the 12th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
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Particle filters have been found to be effective in tracking mobile targets in indoor environments. One frequently encountered problem in these settings occurs when the target's movement pattern changes unexpectedly; such as when the target turns around, enters a room from a corridor or turns left or right at an intersection. If the particle filter makes an incorrect prediction, it might not be able to recover using the normal techniques of prediction, weight update and resampling. We propose an approach to automatically restart the particle filter by sampling the latest trusted observation when the particle cloud diverges too much from the observations. The restart decision is based on Kullback-Leibler divergence between the probability surfaces associated with the current observation and the particle cloud. Through an experimental study we show that the restart algorithm allows the successful early recovery of stranded particle filters, in our scenarios providing a 36% average improvement in localization accuracy.