Aspect invariant features for radar target recognition

Doo, Seung Ho; Smith, Gerald Birney; Baker, Chris

doi:10.1049/iet-rsn.2016.0075

Cited by 8 publications

(6 citation statements)

References 28 publications

(30 reference statements)

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“…According to the concept of high-level classification (El-Darymli et al, 2016), we will consider the feature-based approach that can be implemented as a single multi-class classifier. There is a set of the mathematical models for ATR SAR:…”

Section: Introductionmentioning

confidence: 99%

A classification technique of civil objects by artificial neural networks using estimation of entropy on synthetic aperture radar images

Kvasnov

Shkodyrev

2021

J. Sens. Sens. Syst.

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Abstract. The article discusses the method for the classification of non-moving group objects for information received from unmanned aerial vehicles (UAVs) by synthetic aperture radar (SAR). A theoretical approach to analysis of group objects can be estimated by cross-entropy using a naive Bayesian classifier. The entropy of target spots on SAR images revaluates depending on the altitude and aspect angle of a UAV. The paper shows that classification of the target for three classes able to predict with fair accuracy P=0,964 based on an artificial neural network. The study of results reveals an advantage compared with other radar recognition methods for a criterion of the constant false-alarm rate (PCFAR<0.01). The reliability was confirmed by checking the initial data using principal component analysis.

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Section: Introductionmentioning

confidence: 99%

A classification technique of civil objects by artificial neural networks using estimation of entropy on synthetic aperture radar images

Kvasnov

Shkodyrev

2021

J. Sens. Sens. Syst.

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“…In particular, it was shown that the orientation of the target had a strong influence on the target appearance [14, 15]. Improvement in feature based classification performance was shown when an estimate of the target orientation was provided to the classification algorithm [16, 17]. Deep learning work also benefited from the inclusion of target orientation knowledge to the network training.…”

Section: Introductionmentioning

confidence: 99%

Pose‐informed deep learning method for SAR ATR

Belloni

Aouf

Balleri

et al. 2020

IET Radar, Sonar & Navigation

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“…The latter can rely on episodic and semantic features [21] or sparse robust filters [22] that originate from the human cognition process. Other methods include binary operations [23], using the target's scattering centers [15], [24] or the azimuth and range target profiles fusion [25].…”

Section: Introduction Odern Warfare Requires High Performing Autommentioning

confidence: 99%

Fusing Deep Learning and Sparse Coding for SAR ATR

Kechagias‐Stamatis

Aouf

2019

IEEE Trans. Aerosp. Electron. Syst.

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We propose a multi-modal and multi-discipline data fusion strategy appropriate for Automatic Target Recognition (ATR) on Synthetic Aperture Radar imagery. Our architecture fuses a proposed Clustered version of the AlexNet Convolutional Neural Network with Sparse Coding theory that is extended to facilitate an adaptive elastic net optimization concept. Evaluation on the MSTAR dataset yields the highest ATR performance reported yet which is 99.33% and 99.86% for the 3 and 10-class problems respectively.

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Aspect invariant features for radar target recognition

Cited by 8 publications

References 28 publications

A classification technique of civil objects by artificial neural networks using estimation of entropy on synthetic aperture radar images

A classification technique of civil objects by artificial neural networks using estimation of entropy on synthetic aperture radar images

Pose‐informed deep learning method for SAR ATR

Fusing Deep Learning and Sparse Coding for SAR ATR

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