Impact of phase information on radar automatic target recognition

Date of Award


Degree Name

Ph.D. in Electrical Engineering


Department of Electrical and Computer Engineering


Advisor: Robert Prewitt Penno


Traditional synthetic aperture radar (SAR) systems tend to discard phase information of formed complex radar imagery prior to automatic target recognition (ATR). This practice has historically been driven by available hardware storage, processing capabilities, and data link capacity. Recent advances in high performance computing (HPC) have enabled extremely dense storage and processing solutions. Therefore, previous motives for discarding radar phase information in ATR applications have been mitigated. First, we characterize the value of phase in one-dimensional (1-D) radar range profiles and two dimensional (2-D) SAR imagery with respect to the ability to correctly estimate target features, which are currently employed in ATR algorithms for target discrimination. These features correspond to physical characteristics of a target through radio frequency (RF) scattering phenomenology. Physics-based electromagnetic scattering models developed from the geometrical theory of diffraction are utilized for the information analysis presented here. Information is quantified by the error of target parameter estimates from noisy radar signals when phase is either retained or discarded. Operating conditions (OCs) of signal-to-noise ratio, bandwidth, and aperture extent are considered. Second, we investigate the value of phase in 1-D radar returns with respect to the ability to correctly classify canonical targets. Classification performance is evaluated via three techniques, namely, naïve Bayes, logistic regression and a bound on Bayes error rate (BER). These classification techniques maintain varying assumptions on the observed data set, with the BER bound making no assumptions. In each case, phase information is demonstrated to improve radar target classification rates.


Synthetic aperture radar, Radar targets, Optical pattern recognition, Electrical Engineering, synthetic aperture radar, high range resolution, radar range profiles, phase information, target feature estimation, position accuracy, error variance, Cramer-Rao lower bound, operating conditions, automatic target recognition

Rights Statement

Copyright 2016, author