<title>Mastering interfering scatterers in HRR data for ATR systems</title>

Abstract

The problem of interfering scatters in high range resolution (HRR) radar data is addressed in this paper. We derive, using a scattering center representation of a target, classifiers that can handle the unknown phases of the centers. We also show how to incorporate uncertainties in the magnitudes and positions of the scattering centers. The automatic target recognition (ATR) problem is discussed in a Bayesian setting, and we show how the uncertainties can be handled by such scattering center classifiers. Monte Carlo simulations are used to evaluate the performance and robustness of the classifiers for simple test cases, and data from electromagnetic prediction codes are used to illustrate the behavior on real targets.

<title>Comparison of some HRR-classification algorithms</title>

“…For the Cisoid and Well separated cisoid methods this is discussed in ref. 1. In this paper we do not address the problem of uncertain calibration.…”

“…The back scattered, demodulated and calibrated signal can therefore be written as (1) where f is the frequency, c the speed of light and . Each scattering center, k, is described with its position , phase , amplitude and a parameter .…”

“…We are focusing on ATR of air targets and have previously presented a method for model based recognition using a scattering center representation of targets 1 . In this paper we compare our method with other methods proposed for the same application.…”