Analyzing Radar Cross Section Signatures of Diverse Drone Models at mmWave Frequencies

Semkin, Vasilii; Haarla, Jaakko; Pairon, Thomas; Slezak, Christopher; Rangan, Sundeep; Viikari, Ville; Oestges, Claude

doi:10.1109/access.2020.2979339

Cited by 60 publications

(26 citation statements)

References 20 publications

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Section: Discussionmentioning

confidence: 99%

“…In [13] collected RCS measurement considering different measurement angles through mmWave frequency. Similar to [13] in [19] collected signature through polarimetry Ku-band frequency modulated continuous wave (FMCW) radar system. They converted the entire signal information into the image and then these images were trained using a convolution neural network and then the classification of the drone is performed.…”

Section: Discussionmentioning

confidence: 99%

“…Recent work has shown that these base stations could operate as a radar with small modifications [12]. In [13] provided extensive measurement results of diverse drone models at one possible 5G frequency band along with a study of the particular drone's frame materials. In addition, we estimate the effect of different drone parts on the total RCS values through simulation results.…”

Section: Introductionmentioning

confidence: 99%

“…Following a similar framework to the measurements above, [13] fill the gap and present comprehensive 3D measurements of RCS characteristics of nine UAVs as well as the RCS of the Li-Po batteries, since the batteries are the immense reflective object in the drone configuration. In [13], studied quasi-3D RCS signatures of a diverse range of drone models over the frequency band of 26-40 GHz, which may provide essential material for the drone database [18], [19]. Using this measurement this work aim at developing an automatic detection drone using deep learning technique [25], [26].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Drone Classification Using Radar Cross Section Signatures at mmWave Frequencies

Al-Absi

Kim

et al. 2021

IEEE Access

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This research was a part of the project titled 'Marine digital AtoN information management and service system development(1/5) (20210650)', funded by the Ministry of Oceans and Fisheries, Korea.."ABSTRACT This paper presents drone classification at millimeter-wave (mmWave) radars using the deep learning (DL) technique. The adoption mmWave technology in radar systems enables better resolution and aid in detecting smaller drones. Using radar cross-section (RCS) signature enables us to detect malicious drones and suitable action can be taken by respective authorities. Existing drone classification converts the RCS signature into images and then performs drone classification using a convolution neural network (CNN).Converting every signature into an image induces additional computation overhead; further CNN model is trained considering fixed learning rate. Thus, when using CNN-based drone classification under a highly dynamic environment exhibit poor classification accuracy. This paper present an im-proved long short-term memory (LSTM) by introducing a weight optimization model that can reduce computation overhead by not allowing the gradient to not flow through hidden states of the LSTM model. Further, present adaptive learning rate optimizing (ALRO) model for training the LSTM model. Experiment outcome shows LSTM-ALRO achieves much better drone detection accuracies when compared with the existing CNN-based drone classification model. INDEX TERMSConvolutional neural network, drone detection, micro doppler signature (MDS), millimeter-wave, radar cross-section, unmanned aerial vehicle.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning-Based Drone Classification Using Radar Cross Section Signatures at mmWave Frequencies

Al-Absi

Kim

et al. 2021

IEEE Access

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“…Moreover, when considering a target whose size is comparable to that of a mini/micro-UAV, FMCW scanning radars are usually preferred compared to pulse radars that perform poorly in localizing small radar cross sections (RCSs) [13]. For this reason, some research activities have focused on the assessment of the RCS values of UAVs and their impact on the detection performance [8], [24], [25]. However, how the target RCS affects tracking accuracy and navigation performance is an open issue.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach

2020

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Nowadays there is a growing research interest on the possibility of enriching small flying robots with autonomous sensing and online navigation capabilities. This will enable a large number of applications spanning from remote surveillance to logistics, smarter cities and emergency aid in hazardous environments. In this context, an emerging problem is to track unauthorized small unmanned aerial vehicles (UAVs) hiding behind buildings or concealing in large UAV networks. In contrast with current solutions mainly based on static and on-ground radars, this paper proposes the idea of a dynamic radar network of UAVs for real-time and high-accuracy tracking of malicious targets. To this end, we describe a solution for real-time navigation of UAVs to track a dynamic target using heterogeneously sensed information. Such information is shared by the UAVs with their neighbors via multi-hops, allowing tracking the target by a local Bayesian estimator running at each agent. Since not all the paths are equal in terms of information gathering point-of-view, the UAVs plan their own trajectory by minimizing the posterior covariance matrix of the target state under UAV kinematic and anti-collision constraints. Our results show how a dynamic network of radars attains better localization results compared to a fixed configuration and how the on-board sensor technology impacts the accuracy in tracking a target with different radar cross sections, especially in non line-of-sight (NLOS) situations.

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