Stochastic Resonance and Suboptimal Radar Target Classification

Stochastic resonance has received significant attention recently in the signal processing community with emphasis on signal detection. The basic notion is that the performance of some suboptimal detectors can be improved by adding independent noise to the measured (and already noise contaminated) observation. The notion of adding noise makes sense if the observation is the result of nonlinear processing, and there exist proven scenarios where the signal-to-noise ratio improves by adding independent noise. This paper reviews a set of parametric and nonparametric sub-optimal radar target classification systems and explores (via computer simulation) the impact of adding independent noise to the observation on the performance of such sub-optimal systems. Although noise is not added in an optimal fashion, it does have an impact on the probability of classification error. Real radar scattering data of commercial aircraft models is used in this study. The focus is on exploring scenarios where added noise may improve radar target classification.