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

Guojing He; Jianqi Zhang; Delian Liu; Honghua Chang

doi:10.1364/ao.47.005534

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

Appl Opt. 2008 Oct 10;47(29):5534-40. doi: 10.1364/ao.47.005534.

Authors

Guojing He¹, Jianqi Zhang, Delian Liu, Honghua Chang

Affiliation

¹ School of Technical Physics, Xidian University, 2 South Taibai Road, Xi'an Shaanxi 710071, China. gjhe@mail.xidian.edu.cn

PMID: 18846196
DOI: 10.1364/ao.47.005534

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.