Discrete Mathematics - Topics on domination
Tracking a moving object with a binary sensor network
Proceedings of the 1st international conference on Embedded networked sensor systems
Distributed particle filters for sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Distributed state representation for tracking problems in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
A line in the sand: a wireless sensor network for target detection, classification, and tracking
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Military communications systems and technologies
MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets
IEEE Transactions on Pattern Analysis and Machine Intelligence
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
On target tracking with binary proximity sensors
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Proceedings of the 4th international conference on Embedded networked sensor systems
Tracking multiple targets using binary proximity sensors
Proceedings of the 6th international conference on Information processing in sensor networks
A Sensor Network System for Measuring Traffic in Short-Term Construction Work Zones
DCOSS '09 Proceedings of the 5th IEEE International Conference on Distributed Computing in Sensor Systems
Target tracking with binary proximity sensors
ACM Transactions on Sensor Networks (TOSN)
A distributed algorithm for managing multi-target identities in wireless ad-hoc sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
IEEE Communications Magazine
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Recent work has shown that, despite the minimal information provided by a binary proximity sensor, a network of these sensors can provide remarkably good target tracking performance. In this article, we examine the performance of such a sensor network for tracking multiple targets. We begin with geometric arguments that address the problem of counting the number of distinct targets, given a snapshot of the sensor readings. We provide necessary and sufficient criteria for an accurate target count in a one-dimensional setting, and provide a greedy algorithm that determines the minimum number of targets that is consistent with the sensor readings. While these combinatorial arguments bring out the difficulty of target counting based on sensor readings at a given time, they leave open the possibility of accurate counting and tracking by exploiting the evolution of the sensor readings over time. To this end, we develop a particle filtering algorithm based on a cost function that penalizes changes in velocity. An extensive set of simulations, as well as experiments with passive infrared sensors, are reported. We conclude that, despite the combinatorial complexity of target counting, probabilistic approaches based on fairly generic models of trajectories yield respectable tracking performance.