Deep Learning Approach to the Detection of Scattering Delay in Radar Images

In radar imaging, stochastic target models are routinely used to describe distributed scatterers. In such models, the reflectivity of a target or clutter is a realization of a stochastic process with certain autocorrelation properties. While most targets reflect the impinging electromagnetic radiation instantaneously, some targets with complicated geometry and/or material composition may exhibit delayed scattering. Detecting such delays will provide valuable data for target identification. However, the scattering delay can be confused with the signal propagation delay, and this difference is sometimes rather subtle. Due to the stochastic nature of the radar data, the classification errors are inevitable. The misclassification rate depends on the parameters characterizing the radar system, imaging scene, and observation settings. A convolutional neural network is applied to the problem of discrimination between the instantaneous and delayed targets in synthetic aperture radar images. A trained neural network demonstrates the discrimination quality commensurate with that of the benchmark maximum likelihood-based classifier.