Classification of ground moving radar targets using convolutional neural network

Hadhrami, Esra Al; Mufti, Maha Al; Taha, Bilal; Werghi, Naoufel

doi:10.23919/mikon.2018.8405154

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…The most commonly employed radar signal characteristic for automatic target classification is the micro-Doppler (m-D) signature [40]. The m-D signature has been utilized by many works for automatic target classification such as ground moving target classification [41,42,43], ship detection [44], human gait recognition [45,46], and human activity classification [47,48]. In recent years, it has been an active area of research in the field of c-UAV radar based applications.…”

Section: Radar Sensormentioning

confidence: 99%

Deep Learning on Multi Sensor Data for Counter UAV Applications—A Systematic Review

Samaras

Diamantidou

Ataloglou

et al. 2019

Sensors

147

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Usage of Unmanned Aerial Vehicles (UAVs) is growing rapidly in a wide range of consumer applications, as they prove to be both autonomous and flexible in a variety of environments and tasks. However, this versatility and ease of use also brings a rapid evolution of threats by malicious actors that can use UAVs for criminal activities, converting them to passive or active threats. The need to protect critical infrastructures and important events from such threats has brought advances in counter UAV (c-UAV) applications. Nowadays, c-UAV applications offer systems that comprise a multi-sensory arsenal often including electro-optical, thermal, acoustic, radar and radio frequency sensors, whose information can be fused to increase the confidence of threat’s identification. Nevertheless, real-time surveillance is a cumbersome process, but it is absolutely essential to detect promptly the occurrence of adverse events or conditions. To that end, many challenging tasks arise such as object detection, classification, multi-object tracking and multi-sensor information fusion. In recent years, researchers have utilized deep learning based methodologies to tackle these tasks for generic objects and made noteworthy progress, yet applying deep learning for UAV detection and classification is considered a novel concept. Therefore, the need to present a complete overview of deep learning technologies applied to c-UAV related tasks on multi-sensor data has emerged. The aim of this paper is to describe deep learning advances on c-UAV related tasks when applied to data originating from many different sensors as well as multi-sensor information fusion. This survey may help in making recommendations and improvements of c-UAV applications for the future.

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Section: Radar Sensormentioning

confidence: 99%

Deep Learning on Multi Sensor Data for Counter UAV Applications—A Systematic Review

Samaras

Diamantidou

Ataloglou

et al. 2019

Sensors

147

View full text Add to dashboard Cite

show abstract

“…To get less complex systems, target classification from raw data offers a number of advantages. In this category, we find radar cross section (RCS) responses or micro-Doppler measurements that can be directly used to classify different objects, moving targets or human activities [10][11][12]. Recent works on object classification based on raw SAR measurements have even shown results that are only slightly inferior to pre-processed data, but at much lower computational costs [13,14].…”

Section: Introductionmentioning

confidence: 99%

Shape and orientation classification of objects based on their electromagnetic signatures using convolutional neural networks

Zaky,

Fortino,

Miramond

et al. 2024

Inverse Problems

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This study addresses the classification of objects using their electromagnetic signatures with Convolutional Neural Networks (CNNs) trained on noiseless data. The singularity expansion method (SEM) was applied to establish a compact model that accurately represents the ultra-wideband scattered field of an object, independently of its orientation and observation angle. To perform the classification, we used a CNN associated with a noise-robust SEM technique to classify different objects based on their characteristic parameters. To validate this approach, we compared the performance of the classifier with and without SEM pre-processing of the scattered field for different noise levels and for object sizes not present in the training set. Moreover, we propose a procedure that determines the direction of the receiving antenna and orientation of an object based on the residues associated with each complex natural resonance. This classification procedure using pre-processed SEM data is promising, especially when generalizing to object sizes not included in the training set.

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“…Radar Cross Section (RCS) responses can be directly used as raw data, as in [5], [6], where authors used them to classify objects using Convolutional Neural Networks (CNNs) and SVM respectively. Moreover, Micro-Doppler measurements are used for the detection of humans and the classification of moving targets or human activities [7], [8]. Radar imaging techniques can also be applied to generate pre-processed data where the Synthetic Aperture Radar (SAR) images are deployed for classification [9], [10].…”

Section: Introductionmentioning

confidence: 99%

“…Radar Cross Section responses can be directly used as raw data to classify objects (Bufler et al., 2015). Moreover, Micro‐Doppler measurements are used for the detection of humans and the classification of moving targets or human activities (Hadhrami et al., 2018; Yang et al., 2006). However, those measurements usually depend upon the angle of observation making the classifiers sensible to a change in the object orientation or in the position of the antenna.…”

Section: Introductionmentioning

confidence: 99%

Generalization Ability of Deep Learning Algorithms Trained Using SEM Data for Objects Classification

et al. 2022

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This paper proposes a workflow to efficiently determine the material of spherical objects and the location of the receiving antenna relative to their position in bi-static measurements using supervised learning techniques. From a single observation, we compare classification performances resulting from the application of several classifiers on different data types: the Ultra-Wide Band scattered field in time and frequency domains and pre-processed data from the Singularity Expansion Method (SEM). Indeed, the resonances extracted using the SEM are aspect independent and therefore, are used to discriminate the objects. As for the residues, they depend upon the aspect angle and can hence be exploited to determine the observation angle. We construct 3 datasets to assess which one yields the highest accuracy while using the simplest and fastest classifiers. Hence, 80% of each dataset is used for training and the remaining 20% are used for testing. In a further step, we test with sphere sizes and data with several noisy levels that were not in the training datasets. Although SEM is noise sensitive, associating a robust feature extraction technique (Vector Fitting) with a simple but reliable classifier is promising, particularly when generalizing to data not included in the training set.

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Classification of ground moving radar targets using convolutional neural network

Cited by 10 publications

References 14 publications

Deep Learning on Multi Sensor Data for Counter UAV Applications—A Systematic Review

Deep Learning on Multi Sensor Data for Counter UAV Applications—A Systematic Review

Shape and orientation classification of objects based on their electromagnetic signatures using convolutional neural networks

Generalization Ability of Deep Learning Algorithms Trained Using SEM Data for Objects Classification

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