Clutter metric based on the Cramer-Rao lower bound on automatic target recognition

Abstract

This is a performance evaluation on the implementation of the Cramer-Rao lower bound (CRLB) for background clutter measurement on automatic target recognition (ATR). In essence the background clutter evaluation problem for ATR is consistent with the deterministic parameter estimation problem. Thus, useful concepts and theories of deterministic parameter estimation can be introduced into the investigation of background clutter. In this paper, the CRLB is employed as a metric for clutter measurement. Requirements needed for this application are analyzed, and the approach for obtaining the CRLB of a scene image is produced. The flexibility of the CRLB metric is analyzed. Discussion and comparison are made on the relationship between the CRLB metric and the Sims signal-to-clutter metric. Finally, we illustrate how this metric defines the potential for false alarms by determining the correspondence level between a target and background through the application of the CRLB.

Display Clutter

“…the target-Background similarity perspective of clutter Several techniques are based on quantifying the degree of similarity between the target and the background or other distractor objects (Chang, Zhang, Liu, Yang, & Li, 2010;He et al, 2008;Hilgers, Vockel, Reynolds, & Pickard, 1997;Phillips & Sims, 1997;Xu, Shi, & Luo, 2012;Yang et al, 2011;Yang, Zhang, Xu, Chang, & He, 2007). In one study, for example, clutter was measured by comparing the similarity in luminance, contrast, and structure between the target and the background (Chang & Zhang, 2006).…”

Image Sequence Measures for Automatic Target Tracking

“…He et al developed a background clutter measure for automatic target recognition, based on the theory of Cramer-Rao lower bound (CRLB ) [49]. This metric defines the potential of false alarms by determining the correspondence level between a target and background through the application of the CRLB.…”