&lt;title&gt;Comparison of some HRR-classification algorithms&lt;/title&gt;

Dicander, Fredrik; Jönsson, Roland

doi:10.1117/12.438202

Cited by 9 publications

(6 citation statements)

References 6 publications

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“…The extended target moves at a constant velocity around a circle, and consists of five colinear point scatterers. 2 The Fig. 7.…”

Section: A Extended Targetsmentioning

confidence: 98%

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Target Motion and High Range Resolution Profile Generation

Winters

2012

IEEE Trans. Aerosp. Electron. Syst.

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The problem of forming high range resolution (HRR) profiles from radar data of moving targetsis considered. HRR profiles can result from processing range-Doppler images of targets, whereby the 2-D range-Doppler image is collapsed into a 1-D range profile. The interest in HRR profiles can partly be explained by the hypothesis that they can be used to characterize and discriminate targets. HRR profiles are also of interest because of their potential to be less sensitive than range-Doppler images to target motion. Two different approaches to forming an HRR profile from a range-Doppler image of a moving target are examined. The advantages and disadvantages of each approach are discussed.Results are obtained through analysis, simulation, and an example with synthetic aperture radar (SAR) data.

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“…The extended target moves at a constant velocity around a circle, and consists of five colinear point scatterers. 2 The Fig. 7.…”

Section: A Extended Targetsmentioning

confidence: 98%

“…There has been interest in recent years in the idea of using high range resolution (HRR) profiles to aid in the tracking of high-value targets (HVTs) [1][2][3]. HRR profiles are one-dimensional representations of targets, and can be generated from range-Doppler images constructed from radar data.…”

Section: Introductionmentioning

confidence: 99%

Target Motion and High Range Resolution Profile Generation

Winters

2012

IEEE Trans. Aerosp. Electron. Syst.

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“…Missed Alarm = Declaring (keeping) H0 when H1 holds (28) In the case of (27) False Alarm may result in a wrong pose angle estimate, which is not desirable. This would correspond to a target miss-association possibility.…”

Section: Hypothesis Testing For Steps 21 and 22 32 Hypothesis Testmentioning

confidence: 99%

New Pose Estimation Methodology for Target Tracking and Identification

Hodzic

Namas

2014

scjournal

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Ground Moving Target Indicator (GMTI) and High Resolution Radar (HRR) can track position and velocity of ground moving target. Pose, angle between position and velocity, can be derived from kinematics estimates of position and velocity and it is often used to reduce the search space of a target identification (ID) and Automatic Target Recognition (ATR) algorithms. Due to low resolution in some radar systems, the GMTI estimated pose may exhibit large errors contributing to a faulty identification of potential targets. Our goal is to define new methodology to improve pose estimate. Besides applications in target tracking, there are numerous commercial applications in machine learning, augmented reality and body tracking.

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“…Classifiers have been developed for correlation [10], Bayes and Dempster Shafer information fusion approaches [11], and Neuro-Fuzzy methods [12]. Other approaches include eigen-value template matching [13] and likelihood methods accounting for Rician, amplitude, specular, and diffuse, Cisoid scattering [14].…”

Section: Introductionmentioning

confidence: 99%

Target identification performance improvement from enhanced HRR radar clutter suppression

Kahler

Blasch

2009

Proceedings of the IEEE 2009 National Aerospace &Amp; Electronics Conference (NAECON)

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Airborne radar tracking in moving ground vehicle scenarios is impacted by sensor, target, and environmental dynamics. Moving targets can be assessed with 1-D High Range Resolution (HRR) Radar profiles with sufficient signal-to-noise (SNR) present which contain enough feature information to discern one target from another to help maintain track or to identify the vehicle. Typical radar clutter suppression algorithms developed for processing moving ground target data not only remove the surrounding clutter but also a portion of the target signature. Enhanced clutter suppression can be achieved using a multi-channel signal subspace (MSS) algorithm which preserves target features. In this paper, we exploit extra information from enhanced clutter suppression for automatic target recognition (ATR), present a gain comparison using displaced phase center antenna (DPCA) and MSS clutter suppressed HRR data, and generate confusion-matrix identification results. The results show that more channels for MSS increase ID over DCPA, result in a slightly noisier clutter suppressed image, and preserve more target features after clutter cancellation

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<title>Comparison of some HRR-classification algorithms</title>

Cited by 9 publications

References 6 publications

Target Motion and High Range Resolution Profile Generation

Target Motion and High Range Resolution Profile Generation

New Pose Estimation Methodology for Target Tracking and Identification

Target identification performance improvement from enhanced HRR radar clutter suppression

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