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, 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.

Full Text

Duke Authors

Cited Authors

  • Zhang, G; Ferrari, S; Cai, C

Published Date

  • February 2012

Published In

Volume / Issue

  • 42 / 1

Start / End Page

  • 2 - 16

PubMed ID

  • 22057064

Electronic International Standard Serial Number (EISSN)

  • 1941-0492

Digital Object Identifier (DOI)

  • 10.1109/TSMCB.2011.2165336

Language

  • eng