Probabilistic track coverage in cooperative sensor networks

Authors:
Silvia Ferrari;Guoxian Zhang;Thomas A. Wettergren
Affiliations:
Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC;Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC;Naval Undersea Warfare Center, Newport, RI
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2010

Citing 7
Cited 2

Multidimensional assignment formulation of data association problems arising from multitarget and multisensor tracking

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)
A decision support system for optimal deployment of sonobuoy networks based on sea current forecasts and multi-objective evolutionary optimization

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.