Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Elements of information theory
Elements of information theory
Robust location tracking using a dual layer particle filter
Pervasive and Mobile Computing
Localized Broadcasting with Guaranteed Delivery and Bounded Transmission Redundancy
IEEE Transactions on Computers
Incremental distributed identification of Markov random field models in wireless sensor networks
IEEE Transactions on Signal Processing
Decentralized sigma-point information filters for target tracking in collaborative sensor networks
IEEE Transactions on Signal Processing - Part II
Hidden Markov Models for Radio Localization in Mixed LOS/NLOS Conditions
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
A Bayesian approach to tracking multiple targets using sensorarrays and particle filters
IEEE Transactions on Signal Processing
Particle filters for positioning, navigation, and tracking
IEEE Transactions on Signal Processing
Localization systems for wireless sensor networks
IEEE Wireless Communications
Tracking a moving object via a sensor network with a partial information broadcasting scheme
Information Sciences: an International Journal
An adaptive localisation algorithm of mobile node in wireless sensor network
International Journal of Sensor Networks
Genetic Algorithm-based Adaptive Optimization for Target Tracking in Wireless Sensor Networks
Journal of Signal Processing Systems
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Wireless Sensor Networks are well suited for tracking targets carrying RFID tags in indoor environments. Tracking based on the received signal strength indication (RSSI) is by far the cheapest and simplest option, but suffers from secular biases due to effects of multi-path, occlusions and decalibration, as well as large unbiased errors due to measurement noise. We propose a novel algorithm that solves these problems in a distributed, scalable and power-efficient manner. Firstly, our proposal includes a tandem incremental estimator that learns and tracks the radio environment of the network, and provides this knowledge for the use of the tracking algorithm, which eliminates the secular biases due to radio occlusions etc. Secondly, we reduce the unbiased tracking error by exploiting the co-dependencies in the motion of several targets (as in crowds or herds) via a fully distributed and tractable particle filter. We thereby extract a significant "diversity gain" while still allowing the network to scale seamlessly to a large tracking area. In particular, we avoid the pitfalls of network congestion and severely shortened battery lifetimes that plague procedures based on the joint multi-target probability density.