&lt;title&gt;Analysis of a 1D HRR moving target ATR&lt;/title&gt;

Williams, Robert L.; Westerkamp, John J.; Gross, David C.; Palomino, Adrian P.; Fister, Thomas

doi:10.1117/12.357658

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…The Prony technique generates feature vectors that describe scattering centers of a target. Another template matching effort applied to HRR-derived features was done in [20], [21] and [22]. Here, a leave-one-out method was used to capture the effect of processing a SAR chip that is not in the template library for which the classifier is trained.…”

Section: Syntactic Pattern Recogntionmentioning

confidence: 99%

HRR Signature Classification using Syntactic Pattern Recognition

Turnbaugh¹,

Bauer²,

Oxley³

et al. 2008

2008 IEEE Aerospace Conference

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An Automatic Target Classification system contains a classifier that maps a vector of real numbered features characteristic to a specific target onto a class label. Other features can be a string of symbols or alphabets that may not involve real numbers at all. There are certain orderings of the symbols in the strings governed by syntax rules, thus, generating a language, (that is, a collection of strings). Thus, a classifier would map a string to a class label. Such a classifier is called a syntactical classifier and varies greatly from its vector space counter part. This paper will give an overview of the construction of a grammar that generates a language then shows how they fit into a syntactical classification system. The performances of two syntactical classification systems with two and ten labels respectively are presented via confusion matrices. Experiments performed on public release DCS database indicate this approach has sufficient power to perform target detection using HRR signatures. 12

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Section: Syntactic Pattern Recogntionmentioning

confidence: 99%

HRR Signature Classification using Syntactic Pattern Recognition

Turnbaugh¹,

Bauer²,

Oxley³

et al. 2008

2008 IEEE Aerospace Conference

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“…In matrix form the physical model of the received signal can be written, as a function of the target model M = {w, a} as: x=s(w,a,cb)+n=MAib+n (1) where T The PDF for x is then given by: (2) and we need to integrate over , using a flat prior p(4) = (2ir): (3) to find the PDF that describe the physical model.…”

Section: Definition Of the Physical Modelmentioning

confidence: 99%

“…2 The traditional use of HRR data utilizes only the magnitude of a range profile of the target. It is well known that such range profiles are very sensitive to aspect variations, due to interference between scatters within a range resolution cell.…”

Section: Introductionmentioning

confidence: 99%

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

2000

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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.

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“…Since an accurate false alarm rate cannot be measured without a realistic set of confusers, a leave one out method is used in the ATR evaluation [3]. The algorithm uses a threshold developed by comparing class and pose templates to training data.…”

Section: Moving Target Atrmentioning

confidence: 99%

<title>Continuous identification technology</title>

Diemunsch

2001

Algorithms for Synthetic Aperture Radar Imagery VIII

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The Air Force Research Laboratory's Sensor Directorate is developing the next step in target recognition, a continuous identification capability. This capability consists of a wide range of algorithms, sensor modes and technologies that work in concert to contribute to the overall goal of identification ofmoving as well as stationary targets. Three major pieces ofthis emerging capability are stationary identification, correlation ofthis information with tracks and the moving target recognition technologies. Although a brief discussion of each ofthese pieces will be provided in this paper, the concentration will be on the actual algorithms and technologies ofthe moving target recognition exploiting High Range Resolution (HRR) radar mode and how this complements Ground Moving Target Indication (GMTI) tracking and Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR). This paper will expand on the overall continuous ID vision, work performed under several efforts, new sources of HRR data, and how this data will push the state ofthe art.The advantages of continuous ID should be obvious to the warfighter in many dimensions. As a command and control structure it is very difficult to identify and target time critical targets especially when the identification process is limited to stationary targets. These technologies will provide a wider range oftargeting options so the enemy has no sanctuary in moving, setting up and in hiding.

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<title>Analysis of a 1D HRR moving target ATR</title>

Cited by 5 publications

References 4 publications

HRR Signature Classification using Syntactic Pattern Recognition

HRR Signature Classification using Syntactic Pattern Recognition

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

<title>Continuous identification technology</title>

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