Distributed particle filter (DPF) algorithms are sequential state estimation algorithms that are
executed by a set of agents. Some or all of the agents perform local particle filtering and …

We consider distributed state estimation in a wireless sensor network without a fusion
center. Each sensor performs a global estimation task-based on the past and current …

Statistical inference is a mature research area, but distributed inference problems that arise
in the context of modern wireless sensor networks (WSNs) have new and unique features …

Noncooperative game theory is a branch of game theory for the resolution of conflicts among
interacting decision makers (called players), each behaving selfishly to optimize his own …

Boundary estimation and tracking have important applications in the areas of environmental
monitoring and disaster management. A boundary separates two regions of interest in a …

The problem of modeling and predicting spatiotemporal traffic phenomena over an urban
road network is important to many traffic applications such as detecting and forecasting …

Large-scale traffic systems require techniques that are able to 1) deal with high amounts of
data and heterogenous data coming from different types of sensors, 2) provide robustness in …

Multi-target tracking is a representative real-time application of sensor networks as it exhibits
different aspects of sensor networks such as event detection, sensor information fusion …

This paper presents a novel method for Bayesian bearing-only tracking. Unlike the classical
approaches, which involve using Gaussian distribution, the tracking procedure is completely …

The Markov Chain Distributed Particle Filter (MCDPF) is an algorithm for the nodes in a
sensor network to cooperatively run a particle filter, based on each sensor making updates …