<title>Effects of SAR parametric variations on the performance of automatic target recognition algorithms</title>

Abstract

Synthetic aperture radar (SAR) imagery is one of the most valuable sensor data sources for today's military battlefield surveillance and analysis. The collection of SAR images by various platforms (e.g. Global Hawk, NASA/JPL AIRSAR, etc.) and on various missions for multiple purposes (e.g. reconnaissance, terrain mapping, etc.) has resulted in vast amount of data over wide surveillance areas. The pixel-to-eye ratio is simply too high for human analysts to rapidly sift through massive volumes of sensor data and yield engagement decisions quickly and precisely. Effective automatic target recognition (ATR) algorithms to process this growing mountain of information are clearly needed. However, even after many years of research, SAR ATR still remains a highly challenging research problem. What makes SAR ATR problems difficult is the amount of variability exhibited in the SAR image signatures of targets and clutters. There are many different factors that can cause the variability in SAR image signatures. It is of convention to categorizes those factors into three major groups known as extended operating conditions (OC's) of target, environment and sensor. The group of sensor OC's includes SAR sensor parametric variations in depression angle, polarization, squint angle, frequencies (UHF, VHF, X band) and bandwidth, pulse repetition frequency (PRF), multi-look, antenna geometry and type, image formation algorithms, platform variations and geometric errors, noise level, etc. Many existing studies of SAR ATR have been traditionally focused on the variability of SAR signatures caused by a sub-space of target OC's and environment OC's. The similar studies in terms of SAR parametric variations in sensor OC's have been very limited due to the lack of data across the sensor OC's and the inherent difficulties as well as the high cost in supplying various sensor OC's during the data collections. This paper will present the results of a comprehensive survey of SAR ATR research works involving the subjects of various sensor OC's. We found out in the survey that, to this date, very few research has been devoted to the problems of sensor OC's and their effects over the performance of SAR image based ATR algorithms. Due to the importance of sensor OC's in the ATR applications, we have developed a research platform as well as important focus areas of future research in SAR parametric variations. A number of baseline ATR algorithms in the research platform have been implemented and verified. We have also planned and started SAR data simulation process across the spectrum of sensor OC's. A road-map for the future research of SAR parametric variations (sensor OC's) and their impact on ATR algorithms is laid out in this paper.

A Model for Generating Synthetic VHF SAR Forest Clutter Images

“…However, characterization of target detection system performance over a wide range of operating conditions from measured data alone is an expensive process, requiring the collection of measured target and clutter data for a large number of scenarios. Target detection and false alarm performance are a function of extended operating conditions (OCs) including target variations, environmental conditions, sensor characteristics, and the automatic target recognition (ATR) algorithm [11][12][13]. The availability of clutter models allows one to simulate data to estimate target detection performance over a wider range of environmental operating conditions.…”