Publications [#289487] of Silvia Ferrari

Papers Published

1. Zhang, G; Ferrari, S; Cai, C, A comparison of information functions and search strategies for sensor planning in target classification., Ieee Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the Ieee Systems, Man, and Cybernetics Society, vol. 42 no. 1 (February, 2012), pp. 2-16 [22057064], [doi]
   (last updated on 2021/09/05)

   Abstract:
   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.