An introduction to symbolic dynamics and coding
An introduction to symbolic dynamics and coding
Complexity and real computation
Complexity and real computation
Tracking multiple targets with self-organizing distributed ground sensors
Journal of Parallel and Distributed Computing
Decentralized Multiple Target Tracking Using Netted Collaborative Autonomous Trackers
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Implementing Large-Scale Autonomic Server Monitoring Using Process Query Systems
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Introduction to probabilistic automata (Computer science and applied mathematics)
Introduction to probabilistic automata (Computer science and applied mathematics)
Computer
On the complexity of space bounded interactive proofs
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
The theory of trackability and robustness for process detection
The theory of trackability and robustness for process detection
Survey A survey of computational complexity results in systems and control
Automatica (Journal of IFAC)
On maximum a posteriori estimation of hidden Markov processes
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Discrete Applied Mathematics
Analysis of Deterministic Tracking of Multiple Objects Using a Binary Sensor Network
ACM Transactions on Sensor Networks (TOSN)
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In this article, we formalize the concept of tracking in a sensor network and develop a quantitative theory of trackability of weak models that investigates the rate of growth of the number of consistent tracks given a temporal sequence of observations made by the sensor network. The phenomenon being tracked is modelled by a nondeterministic finite automaton (a weak model) and the sensor network is modelled by an observer capable of detecting events related, typically ambiguously, to the states of the underlying automaton. Formally, an input string of symbols (the sensor network observations) that is presented to a nondeterministic finite automaton, M, (the weak model) determines a set of state sequences (the tracks or hypotheses) that are capable of generating the input string. We study the growth of the size of this candidate set of tracks as a function of the length of the input string. One key result is that for a given automaton and sensor coverage, the worst-case rate of growth is either polynomial or exponential in the number of observations, indicating a kind of phase transition in tracking accuracy. These results have applications to various tracking problems of recent interest involving tracking phenomena using noisy observations of hidden states such as: sensor networks, computer network security, autonomic computing and dynamic social network analysis.