Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Sensor deployment strategy for target detection
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Connecting the Physical World with Pervasive Networks
IEEE Pervasive Computing
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition)
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Sensor deployment and target localization in distributed sensor networks
ACM Transactions on Embedded Computing Systems (TECS)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Distributed detection in a large wireless sensor network
Information Fusion
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Optimal placement of distributed sensors against moving targets
ACM Transactions on Sensor Networks (TOSN)
Probabilistic track coverage in cooperative sensor networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Decentralized detection in sensor networks
IEEE Transactions on Signal Processing
Optimal sensor placement and motion coordination for target tracking
Automatica (Journal of IFAC)
Sensor planning for elusive targets
Mathematical and Computer Modelling: An International Journal
IEEE Communications Magazine
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We consider the optimal deployment of a sparse network of sensors against moving targets, under multiple conflicting objectives of search. The sensor networks of interest consist of sensors which perform independent binary detection on a target, and report detections to a central control authority. A multiobjective optimization framework is developed to find optimal trade-offs as a function of sensor deployment, between the conflicting objectives of maximizing the Probability of Successful Search (PSS) and minimizing the Probability of False Search (PFS), in a bounded search region of interest. The search objectives are functions of unknown sensor locations (represented parametrically by a probability density function), given sensor performance parameters, statistical priors on target behavior, and distributed detection criteria. Numerical examples illustrating the utility of this approach for varying target behaviors are given.