Computational Optimization and Applications
A Geometric Transversal Approach to Analyzing Track Coverage in Sensor Networks
IEEE Transactions on Computers
Transmission rate allocation in multisensor target tracking over a shared network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A neural network approach to complete coverage path planning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Supervisory control of mobile sensor networks: math formulation, simulation, and implementation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the performance of distributed Neyman-Pearson detection systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Energy-efficient deployment of Intelligent Mobile sensor networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Optimal multiobjective placement of distributed sensors against moving targets
ACM Transactions on Sensor Networks (TOSN)
Expert Systems with Applications: An International Journal
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The quality of service of a network performing cooperative track detection is represented by the probability of obtaining multiple elementary detections over time along a target track. Recently, two different lines of research, namely, distributed-search theory and geometric transversals, have been used in the literature for deriving the probability of track detection as a function of random and deterministic sensors' positions, respectively. In this paper, we prove that these two approaches are equivalent under the same problem formulation. Also, we present a new performance function that is derived by extending the geometric-transversal approach to the case of random sensors' positions using Poisson flats. As a result, a unified approach for addressing track detection in both deterministic and probabilistic sensor networks is obtained. The new performance function is validated through numerical simulations and is shown to bring about considerable computational savings for both deterministic and probabilistic sensor networks.