Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Distributed particle filters for sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Analysis of parallelizable resampling algorithms for particle filtering
Signal Processing
Detection and tracking using wireless sensor networks
Proceedings of the 5th international conference on Embedded networked sensor systems
First experiences using wireless sensor networks for noise pollution monitoring
Proceedings of the workshop on Real-world wireless sensor networks
An Elderly Health Care System Using Wireless Sensor Networks at Home
SENSORCOMM '09 Proceedings of the 2009 Third International Conference on Sensor Technologies and Applications
Nonparametric belief propagation
Communications of the ACM
SOI-KF: Distributed Kalman Filtering With Low-Cost Communications Using the Sign of Innovations
IEEE Transactions on Signal Processing
Target Tracking by Particle Filtering in Binary Sensor Networks
IEEE Transactions on Signal Processing
Resampling algorithms and architectures for distributed particle filters
IEEE Transactions on Signal Processing
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The use of distributed particle filters for tracking in sensor networks has become popular in recent years. The distributed particle filters proposed in the literature up to now are only approximations of the centralized particle filter or, if they are a proper distributed version of the particle filter, their implementation in a wireless sensor network demands a prohibitive communication capability. In this work, we propose a mathematically sound distributed particle filter for tracking in a real-world indoor wireless sensor network composed of low-power nodes. We provide formal and general descriptions of our methodology and then present the results of both real-world experiments and/or computer simulations that use models fitted with real data. With the same number of particles as a centralized filter, the distributed algorithm is over four times faster, yet our simulations show that, even assuming the same processing speed, the accuracy of the centralized and distributed algorithms is practically identical. The main limitation of the proposed scheme is the need to make all the sensor observations available to every processing node. Therefore, it is better suited to broadcast networks or multihop networks where the volume of generated data is kept low, e.g., by an adequate local pre-processing of the observations.