Automatic Target Recognition Performance Research Articles

Adaptive multisensor target detection using feature-based fusion

Target detection techniques play an important role in automatic target recognition (ATR) systems because overall ATR performance depends closely on detection results. We propose a multisensor target detection technique based on adaptive feature-based fusion suitable to detect low-contrast and/or blurry targets with relatively low computational complexity. The proposed technique extracts four different features designed to find regions of high contrast, strong edges, or high information content. The proposed multisensor target-detection technique incorporates multiple imaging sensors in different spectral ranges, such as the visible, near-infrared, and far-infrared bands. The images from the multiple sensors are jointly processed via feature extraction and multisensor fusion. In the multisensor fusion process, a multisensor confidence image is created by adaptively combining the features generated from the multisensor images to obtain more accurate potential target locations. Experimental results on multisensor test sequences are provided.

Predicting an upper bound on SAR ATR performance

We present a method for predicting a tight upper bound on performance of a vote-based approach for automatic target recognition (ATR) in synthetic aperture radar (SAR) images. In such an approach, each model target is represented by a set of SAR views, and both model and data views are represented by locations of scattering centers. The proposed method considers data distortion factors such as uncertainty, occlusion, and clutter, as well as model factors such as structural similarity. Firstly, we calculate a measure of the similarity between a given model view and each view in the model set, as a function of the relative transformation between them. Secondly we select a subset of possible erroneous hypotheses that correspond to peaks in similarity functions obtained in the first step. Thirdly, we determine an upper bound on the probability of correct recognition by computing the probability that every selected hypothesis gets less votes than those for the model view under consideration. The proposed method is validated using MSTAR public SAR data, which are obtained under different depression angles, configurations, and articulations.

Superresolution HRR ATR with high definition vector imaging

A new 1-D template-based automatic target recognition (ATR) algorithm is developed and tested on high range resolution (HRR) profiles formed from synthetic aperture radar (SAR) images of targets taken from the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set. In this work, a superresolution technique known as High Definition Vector Imaging (HDVI) is applied to the HRR profiles before the profiles are passed through ATR classification. The new I-D ATR system using HDVI demonstrates significantly improved target recognition compared with previous I-D ATR systems that use conventional image processing techniques. This improvement in target recognition is quantified by improvement in probability of correct classification (PCC). More importantly, the application of HDVI to HRR profiles helps to maintain the same ATR performance with reduced radar resource requirements.

ATR Performance Modeling and Estimation

Dudgeon, Dan E., ATR Performance Modeling and Estimation, Digital Signal Processing10 (2000), 269–285.This paper provides an overview of approaches and issues in modeling the performance of automatic target recognition (ATR) systems. The need to understand ATR performance capability is important under circumstances in which significant testing is not possible, for example, because of the amount of data required to be collected to accurately assess performance and its associated cost or because of denied targets and ground environments. One would like to have a “theory” to extrapolate ATR performance from a well-characterized set of data to situations likely to be confronted in real military operations. Unfortunately no such theory exists, in part because of the difficulty of characterizing and representing in a computational model the inherent variability of target signatures and the backgrounds in which they are embedded.

Automatic target recognition of time critical moving targets using 1D high range resolution (HRR) radar

Synthetic Aperture Radar (SAR) imaging and Automatic Target Recognition (ATR) of moving targets pose a significant challenge due to the inherent difficulty of focusing moving targets. As a result, ATR of moving targets has recently received increased interest. High Range Resolution (HRR) radar mode offers an approach for recognizing moving targets by forming focused HRR profiles with significantly enhanced target-to-(clutter+noise) (T/(C+N)) via Doppler filtering and/or clutter cancellation. A goal of HRR ATR transition is the implementation and evaluation of algorithms exhibiting robustness under extended operating conditions (EOC). The public domain Moving and Stationary Target Acquisition and Recognition (MSTAR) data set was used to study 1D template-based ATR development and performance. Due to the unavailability of a statistically significant moving ground target data set, this approach was taken as an interim step in assessing the separability of ground targets when using range only discriminants. This report summarizes the data and algorithm methodology, simulated performance results, and recommendations.

Performance of 10- and 20-target MSE classifiers

MIT Lincoln Laboratory is responsible for developing the ATR (automatic target recognition) system for the DARPA-sponsored SAIP program; the baseline ATR system recognizes 10 GOB (ground order of battle) targets; the enhanced version of SAIP requires the ATR system to recognize 20 GOB targets. This paper presents ATR performance results for 10- and 20-target mean square error (MSE) classifiers using high-resolution SAR (synthetic aperture radar) imagery.

Demonstration of improved automatic target-recognition performance by moment analysis of correlation peaks

We report the results of experimental tests of an optical-correlator-based automatic target recognition (ATR) system that uses the correlation-peak moment analysis technique of Caprari [Appl. Opt. 38, 1317 (1999)] to assist in discrimination between target and clutter peaks. The ATR system and its operation are briefly described with particular attention devoted to a practical scheme for enhancing the basic ATR system with correlation-peak moment detectors. We investigate the variation of detection and false-alarm rates of moment detectors with moment threshold values. For fixed moment thresholds, we present receiver operating characteristics of both basic and enhanced systems as the conventionally used correlation-peak energy threshold is varied. Results demonstrate that correlation-peak moment analysis materially improves ATR system target-detection performance.

Effects of polarization and resolution on SAR ATR

Lincoln Laboratory is investigating the detection and classification of stationary ground targets using high resolution, fully polarimetric, synthetic aperture radar (SAR) imagery. A study is summarized in which data collected by the Lincoln Laboratory 33 GHz SAR were used to perform a comprehensive comparison of automatic target recognition (ATR) performance for several polarization/resolution combinations. The Lincoln Laboratory baseline ATR algorithm suite was used, and was optimized for each polarization/resolution case. Both the HH polarization alone and the optimal combination of HH, HV, and VV were evaluated; the resolutions evaluated were 1 ft/spl times/1 ft and 1 m/spl times/1 m. The data set used for this study contained approximately 74 km/sup 2/ of clutter (56 km/sup 2/ of mixed clutter plus 18 km/sup 2/ of highly cultural clutter) and 136 tactical target images (divided equally between tanks and howitzers).

A new approach to automatic target recognition

The fundamental problems of automatic target recognition (ATR) are discussed. A new approach to ATR is suggested that includes: a new method of scoring ATR performance, a new concept of artificial images, a new method called probing for extracting target signature knowledge from image experts, and suggestions for coping with the problem of insufficient test data and algorithm obsolescence. >

Image characterization for automatic target recognition algorithm evaluations

Automatic object recognizer algorithms designed to work with imaging sensor inputs require extensive testing before they should be considered robust enough for challenging applications such as military targeting. Testing automatic target recognition (ATR) algorithms in most cases has been limited to a handful of the scenario conditions of interest as represented by imagery collected with a desired imaging sensor. The question naturally arises as to how robust the performance of the ATR is for all scenario conditions of interest, not just for a small set of collected imagery. A way of addressing algorithm robustness is to characterize the input imagery in terms of some common information content or quality measures that can be correlated with ATR performance. This paper addresses the utility of image characterization measures in terms of estimating ATR detection performance by correlation analyses between nine different image measures and the detection responses of two ATR algorithms. Results show that an image measure called target-to-background entropy difference is the best single measure for estimating ATR detection performance, with correlation coefficients as large as 0.60.