Statistical characterization of scattering delay in synthetic aperture radar imaging

TL;DR

This paper improves statistical methods for distinguishing between instantaneous and delayed scatterers in SAR images by establishing confidence levels without assuming specific contrast statistics.

Contribution

It enhances previous maximum likelihood discrimination methods by incorporating confidence levels that adapt to different target contrasts without statistical assumptions.

Findings

Improved discrimination accuracy in SAR imaging.

Confidence levels enhance target classification reliability.

Method applicable across various target contrasts.

Abstract

Distinguishing between the instantaneous and delayed scatterers in synthetic aperture radar (SAR) images is important for target identification and characterization. To perform this task, one can use the autocorrelation analysis of coordinate-delay images. However, due to the range-delay ambiguity the difference in the correlation properties between the instantaneous and delayed targets may be small. Moreover, the reliability of discrimination is affected by speckle, which is ubiquitous in SAR images, and requires statistical treatment. Previously, we have developed a maximum likelihood based approach for discriminating between the instantaneous and delayed targets in SAR images. To test it, we employed simple statistical models. They allowed us to simulate ensembles of images that depend on various parameters, including aperture width and target contrast. In the current paper, we enhance our previously developed methodology by establishing confidence levels for the discrimination between the instantaneous and delayed scatterers. Our procedure takes into account the difference in thresholds for different target contrasts without making any assumptions about the statistics of those contrasts.