Tracking in wireless sensor networks using particle filtering: physical layer considerations
IEEE Transactions on Signal Processing
Sensor selection for target tracking in binary sensor networks using particle filter
Sarnoff'10 Proceedings of the 33rd IEEE conference on Sarnoff
Energy aware iterative source localization for wireless sensor networks
IEEE Transactions on Signal Processing
An improvement on resampling algorithm of particle filters
IEEE Transactions on Signal Processing
On optimal arrangements of binary sensors
COSIT'11 Proceedings of the 10th international conference on Spatial information theory
Robust tracking algorithm for wireless sensor networks based on improved particle filter
Wireless Communications & Mobile Computing
Doppler effect on target tracking in wireless sensor networks
Computer Communications
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We present particle filtering algorithms for tracking a single target using data from binary sensors. The sensors transmit signals that identify them to a central unit if the target is in their neighborhood; otherwise they do not transmit anything. The central unit uses a model for the target movement in the sensor field and estimates the target's trajectory, velocity, and power using the received data. We propose and implement the tracking by employing auxiliary particle filtering and cost-reference particle filtering. Unlike auxiliary particle filtering, cost-reference particle filtering does not rely on any probabilistic assumptions about the dynamic system. In the paper, we also extend the method to include estimation of constant parameters, and we derive the posterior Cramer-Rao bounds (PCRBs) for the states. We show the performances of the proposed methods by extensive computer simulations and compare them to the derived bounds.