title: Fisher information as a metric of locally optimal processing and stochastic resonance.
abstract: The origins of Fisher information are in its use as a performance measure for parametric estimation. We augment this and show that the Fisher information can characterize the performance in several other significant signal processing operations. For processing of a weak signal in additive white noise, we demonstrate that the Fisher information determines (i) the maximum output signal-to-noise ratio for a periodic signal; (ii) the optimum asymptotic efficacy for signal detection; (iii) the best cross-correlation coefficient for signal transmission; and (iv) the minimum mean square error of an unbiased estimator. This unifying picture, via inequalities on the Fisher information, is used to establish conditions where improvement by noise through stochastic resonance is feasible or not.
authors.affiliation: College of Automation Engineering, Qingdao University, Qingdao, People's Republic of China. fabing.duan@gmail.com
authors.forename: Fabing
authors.initials: F
authors.lastname: Duan
authors.forename: François
authors.initials: F
authors.lastname: Chapeau-Blondeau
authors.forename: Derek
authors.initials: D
authors.lastname: Abbott
mesh_terms: D003198:Computer Simulation; D015233:Models, Statistical; D059629:Signal-To-Noise Ratio; D013269:Stochastic Processes; D014732:Vibration