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

Williams, Robert L.; Westerkamp, John J.; Gross, David C.; Palomion, A.; Fister, Thomas

doi:10.1109/nrc.1999.767267

Cited by 28 publications

(20 citation statements)

References 2 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 the classic HRRP coherent averaging method and related methods [4,5,24,25], the averaged HRRP in a small aspect region is utilised as a template. The template is stable in a small aspect region and its SNR can be improved.…”

Section: Introductionmentioning

confidence: 99%

Radar high‐resolution range profile recognition via geodesic weighted sparse representation

Wang

Chen

2015

IET Radar, Sonar & Navigation

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One of the radar high-resolution range profile (HRRP) recognition issues is the target-aspect sensitivity. Both theoretical analysis and real-world data show that the HRRP shows a high correlation only in a very small aspect region. To overcome this problem, in traditional methods, the scattering centre model and averaged range profile are utilised. In this study, the authors present a graph-based semi-supervised method, called geodesic weighted sparse representation (GWSR), to overcome the target-aspect sensitivity problem. It is assumed that HRRP from different targets is located on different manifolds and the correlation information is utilised to separate these manifolds. In GWSR, the geodesic distance is calculated firstly and then the labelled HRRP is reconstructed by the geodesic weight. The nonlinear structure of HRRP can be transformed into a linear one through the reconstruction process. Then, the unlabelled HRRP is sparsely reconstructed and the sparse reconstruction weight can be utilised to estimate the label of the unlabelled HRRP from the given labels. Experiments on three kinds of ground target HRRPs with different backgrounds demonstrate the effectiveness of the authors' method.

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“…Many recognition algorithms have been proposed and performance results published in the literature to address the combat identification problem for both moving and stationary targets. The majority of the identification results have concentrated on single sensor measured and synthetic EO, IR, and SAR data [2,3,4,5,6,7]. Much of this work focused on a single sensor look at an area of interest to produce object identification decisions from one dimensional (1-D) signatures and two dimensional (2-D) images [8].…”

Section: Introductionmentioning

confidence: 99%

Predicted radar/optical feature fusion gains for target identification

Kahler

Blasch

2010

Proceedings of the IEEE 2010 National Aerospace &Amp; Electronics Conference

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Airborne tracking and identification (ID) of high value ground targets is a difficult task impacted by sensor, target, and environmental conditions. Layered sensing, using a combination of standoff and short-range sensors, maintains target track and identification in cluttered environments such as cities or densely vegetated areas through sensor diversity. Data, feature, decision, or information fusion is necessary for high confidence target classification to be achieved using multiple sensors and sensor modalities. Target identification performance is improved by exploiting the extra information gained from independent sensing modalities through information fusion for automatic target recognition (ATR). Increased target ID has been demonstrated using spatial-temporal multi-look sensor fusion and decision level fusion. To further enhance target ID performance and increase decision confidence, feature level fusion techniques are being investigated. A fusion performance model for feature level fusion was applied to a combination of sensor types and features to provide estimates of a fusion gain. This paper presents a fusion performance gain for Synthetic Aperture Radar (SAR), electro-optical (EO), and infrared (IR) video stationary target identification.Keywords: decision level fusion, feature level fusion, electro-optical (EO), infrared (IR), synthetic aperture radar (SAR), information fusion, automatic target recognition (ATR), national imagery interpretability rating scale (NIIRS).

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

Cited by 28 publications

References 2 publications

HRR Signature Classification using Syntactic Pattern Recognition

HRR Signature Classification using Syntactic Pattern Recognition

Radar high‐resolution range profile recognition via geodesic weighted sparse representation

Predicted radar/optical feature fusion gains for target identification

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