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Aircraft recognition from features extracted from measured and simulated radar range profiles

High range resolution (HRR) radar produces high resolution target signatures of moving ground targets that can be separated from ground clutter using Doppler processing. As a result, HRR sensors are a leading candidate to provide all weather, day/night, long-standoff identification of moving ground targets. Previous automatic target recognition (ATR) studies have demonstrated the usefulness of HRR ATR with traditional statistical techniques such as mean-squared error template-based classifiers. This paper presents the results on an initial investigation of the use of artificial neural network (ANN) classifiers for HRR ATR in a continuous tracking (CT) scenario.

A neural network ATR for high range resolution radar signature recognition of moving ground targets

The military is interested in tracking ground-based moving targets from standoff distances for national security and protection of assets. Air reconnaissance platforms, under normal conditions, operate at standoff distances translating to depression angles of less than 15 degrees. At these low depression angles, simple range extent models may be derived from the two-dimensional size of the target. As sensor platforms migrate to space and as this technology is used in a combat mode then models that are robust to higher depression angles are required. In this paper we discuss a model-based approach to range extent estimation using scattering centers extracted using Xpatch, a high frequency electromagnetic scattering code. This approach uses high HRR profiles generated from scattering centers to window the HRR signal to aid in the determination of the range extent and is based on the signal levels inside and outside the window (signal versus clutter). In this study we use measured HRR profile data collected at National Ground Intelligence Center (NGIC) and Xpatch synthetically generated signatures from a tank at 15 and 20 degrees depression angle.

Model-based range extent for feature aided tracking

As a complementary mode to synthetic aperture radar (SAR), high range resolution (HRR) is mainly developed to identify moving ground targets automatically. Previous studies have demonstrated many promising solutions in HRR automatic target recognition (ATR). Most of them popularly build on template-based approach and employ mean square error (MSE) metric in matching algorithm to measure the likelihood between test profiles and template profiles. However, discussion about whether MSE works as it is expected is inadequate. This paper represents a preliminary investigation into MSE metric used in matching algorithm. We firstly illustrate MSE metric theoretically, and then indicate its shortcomings such as random coefficients distorting the difference between profiles in mathematical calculation, excessive emphasis on large difference, and lack of reasonable interpretation in physical distance space. An alternative metric named average absolute error (AAE) is utilized to replace MSE. It is directly based on the distance between profiles and is able to provide a clear physical interpretation. The experiment results show that no matter estimated aspect angles are given or not during classification, AAE-based HRR ATR significantly outperforms the baseline MSE-based HRR ATR.

Improving high range resolution ATR performance with average absolute error metric

Analysis and acceleration for target recognition

This paper smmnarizes a preliminary investigation into a space-based multi-class system-oriented High Range Resolution (HRR) Automatic Target Recognition (ATR) algorithm. Recent conflicts have highlighted the need to detect, track and recognize time critical moving ground targets in all-weather standoff (airborne and space-based) sensor platforms. Current sensor technologies based on Moving Target Indication (MTI) and/or Synthetic Aperture Radar (SAR) imaging are inadequate. While MT1 provides the ability to detect and track ground moving targets, it lacks target discrimination capability due to limited bandwidth. SAR has target discrimination capability for stationary targets, but target motion will blur the image. As a result, ATR of ground moving targets using HRR radar has recently received increased interest within the Air Force. The baseline algorithms presented in this paper are template-based mean squared error classifiers. The paper includes a brief introduction into recent HRR ATR efforts at the Air Force Research Laboratory Sensors Directorate's ATR Assessment Branch (AFRL/SNAA). Motivations and limitations for space- based sensors are discussed along with potential sensor fusion algorithms. The paper concludes by discussing the advantages of HRR ATR for feature aided

Dave Gross

Papers

High range resolution ground moving target atr using advanced space-based sar/mti concepts