A Comparison of Information Functions and Search Strategies for Sensor Planning in Target Classification

This paper investigates the comparative performance of several information-driven search strategies and decision rules using a canonical target classification problem. Five sensor models are considered: one obtained from classical estimation theory and four obtained from Bernoulli, Poisson, binomial...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2012-02, Vol.42 (1), p.2-16
Main Authors: Guoxian Zhang, Ferrari, S., Chenghui Cai
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Classification
Computer Simulation
Cybernetics
Data Mining - methods
Decision Support Techniques
detection
Entropy
information driven
information theory
management
Mathematical analysis
Mathematical models
Models, Theoretical
Mutual information
optimal
Pattern Recognition, Automated - methods
Planning
Random variables
search
Search problems
Searching
sensor
Sensors
Strategy
target
Target tracking
Time measurement
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Summary:This paper investigates the comparative performance of several information-driven search strategies and decision rules using a canonical target classification problem. Five sensor models are considered: one obtained from classical estimation theory and four obtained from Bernoulli, Poisson, binomial, and mixture-of-binomial distributions. A systematic approach is presented for deriving information functions that represent the expected utility of future sensor measurements from mutual information, Rènyi divergence, Kullback-Leibler divergence, information potential, quadratic entropy, and the Cauchy-Schwarz distance. The resulting information-driven strategies are compared to direct-search, alert-confirm, task-driven (TS), and log-likelihood-ratio (LLR) search strategies. Extensive numerical simulations show that quadratic entropy typically leads to the most effective search strategy with respect to correct-classification rates. In the presence of prior information, the quadratic-entropy-driven strategy also displays the lowest rate of false alarms. However, when prior information is absent or very noisy, TS and LLR strategies achieve the lowest false-alarm rates for the Bernoulli, mixture-of-binomial, and classical sensor models.
ISSN:1083-4419
2168-2267
1941-0492
2168-2275
DOI:10.1109/TSMCB.2011.2165336