2024-03-28T16:14:54Z
https://zenodo.org/oai2d
oai:zenodo.org:3596567
2020-01-20T15:39:34Z
user-alfa
user-eu
Krueckemeier, Markus
Schwartau, Fabian
Schoebel, Joerg
2019-09-23
<p>This paper presents a completely passive system for detection and localization of small aircraft and UAVs. The system makes use of the fact that almost any such target emits some kind of RF emission, either by active transmissions or by passive reflection of other sources. Active transmissions can for example be caused by telemetry or video downlinks to a remote control or some kind of unintended emission like a mobile phone carried by a passenger. These transmissions can be identified by means of passive detection. At the same time, passive reflections of signals radiated by illuminators of opportunity like FM radio stations can be used to build a multistatic passive radar.</p>
https://doi.org/10.5281/zenodo.3596567
oai:zenodo.org:3596567
eng
Zenodo
https://zenodo.org/communities/eu
https://zenodo.org/communities/alfa
https://doi.org/10.5281/zenodo.3596566
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
2019 Kleinheubach Conference, Miltenberg, Germany
Antenna arrays
Radio frequency
Passive radar
Switches
Array signal processing
Frequency modulation
Hardware
A modular localization system combining passive RF detection and passive radar
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:3460244
2020-01-20T17:07:31Z
user-alfa
user-eu
Bert van den Broek
Jos van der Velde
Michiel van den Baar
Loek Nijsten
Rob van Heijster
2019-09-25
<p>We present a study of border surveillance systems for automatic threat estimation. The surveillance systems should allow border control operators to be triggered in time so that adequate responses are possible. Examples of threats are smuggling, possibly by using small vessels, cars or drones, and threats caused by unwanted persons (e.g. terrorists) crossing the border. These threats are revealed by indicators which are often not exact and evidence for these indicators incorporates significant amounts of uncertainty. This study is linked to the European Horizon 2020 project ALFA, which focuses on the detection and threat evaluation of low flying objects near the strait of Gibraltar. Several methods are discussed to fuse the indicators while taking the uncertainty into account, including Fuzzy Reasoning, Bayesian Reasoning, and Dempster-Shafer Theory. In particular the Dempster-Shafer Theory is elaborated since this approach incorporates evaluation of unknown information next to uncertainty. The method is based on belief functions representing the indicators. These functions show a gradual increase or decrease of the suspiciousness depending on input parameters such as object speed, size etc. The fusion methods give two output values for each track: a suspect probability and an uncertainty value. The complete dynamic risk assessment of detected flying objects is evaluated by the automatic system and targets with probabilities exceeding a certain threshold and appropriate uncertainty values are presented to the border control operators.</p>
https://doi.org/10.5281/zenodo.3460244
oai:zenodo.org:3460244
eng
Zenodo
https://zenodo.org/communities/eu
https://zenodo.org/communities/alfa
https://doi.org/10.5281/zenodo.3460243
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
10, (2019-09-25)
SPIE, SPIE Defense & Security 2019, Strasbourg, France, 9-12 September
treat evaluation
automation
uncertainty
border security
Automatic threat evaluation for border security and surveillance
info:eu-repo/semantics/article
oai:zenodo.org:3522157
2020-01-20T17:06:08Z
user-alfa
user-eu
Krueckemeier, Markus
Schwartau, Fabian
Monka-Ewe, Carsten
Schoebel, Joerg
2019-10-29
<p>This paper describes the necessary means to combine multiple Ettus Research USRP X310 software-defined radios to a multichannel coherent receiver for direction-of-arrival (DoA) and passive radar applications. The requirements to combine several software-defined radios to a multichannel coherent receiver are examined in general. In particular the requirement of phase coherence necessitates a closer look on the receiver synchronization, since the straightforward approach of synchronizing the systems with a common reference clock will in most cases lead to phase ambiguities between the channels. The mechanism inducing these phase ambiguities between several systems that are phase-locked to a common reference is discussed in detail. Results regarding the achieved phase stability and a preliminary measurement demonstrating the DoA capabilities of the system are shown.</p>
https://doi.org/10.1109/SDS.2019.8768634
oai:zenodo.org:3522157
eng
Zenodo
https://zenodo.org/communities/eu
https://zenodo.org/communities/alfa
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
SDS, 2019 Sixth International Conference on Software Defined Systems, Rome, Italy, 10-13 June 2019
Synchronization of multiple USRP SDRs for coherent receiver applications
info:eu-repo/semantics/preprint
oai:zenodo.org:3821139
2020-05-13T20:20:37Z
user-alfa
user-eu
Fernandes, Luis
Fernandes, Armando
Baptista, Marcia
Chaves, Paulo
2019-06-29
<p>As maritime smuggling is being combatted more effectively, the criminal “modus operandi” consists more frequently of using small aircraft and drones for drug transport. To address this issue, we report our efforts to develop a system capable of accurately tracking suspicious flying objects and identifying them on video streams. Our solution consists in coupling classical computer vision with deep learning to perform tracking and object detection. A discrete Kalman filter is used to predict the location of each object being tracked while the Hungarian algorithm is used to match objects between successive frames. Whenever a potential target is considered suspicious the input images are zoomed and fed into a deep learning pipeline that separates images into the classes aircraft, drones, birds or clouds. A literature survey indicates that this problem with important applications is yet to be fully explored.</p>
https://doi.org/10.5281/zenodo.3821139
oai:zenodo.org:3821139
https://doi.org/10.1109/ECAI46879.2019.9042167
eng
Zenodo
https://zenodo.org/communities/eu
https://zenodo.org/communities/alfa
https://doi.org/10.5281/zenodo.3821138
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ECAI 2019, Electronics, Computers and Artificial Intelligence, Pitesti, Romania, 27-29 June 2019
Object Tracking and Detection,
Deep learning
Convolutional Neural Networks
Residual Networks
Drone, Aircraft and Bird Identification in Video Images Using Object Tracking and Residual Neural Networks
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:3821145
2020-05-13T20:20:37Z
user-alfa
user-eu
Baptista, Marcia
Fernandes, Luis
Chaves, Paulo
2020-01-10
<p>Unauthorized drone flying can prompt disruptions in critical facilities such as airports or railways. To prevent these situations, we propose a surveillance system that can sense malicious and/or illicit aerial targets. The idea is to track moving aerial objects using a static camera and when a tracked object is considered suspicious, the camera zooms in to take a snapshot of the target. This snapshot is then classified as an aircraft, drone, bird or cloud. In this work, we propose the classical technique of two-frame background subtraction to detect moving objects. We use the discrete Kalman filter to predict the location of each object and the Jonker-Volgenant algorithm to match objects between consecutive image frames. A deep residual network, trained with transfer learning, is used for image classification. The residual net ResNet-50 developed for the ILSVRC competition was retrained for this purpose. The performance of the system was evaluated with positive results in real-world conditions. The system was able to track multiple aerial objects with acceptable accuracy and the classification system also exhibited high performance.</p>
https://doi.org/10.5281/zenodo.3821145
oai:zenodo.org:3821145
https://doi.org/10.1007/978-3-030-38822-5_18
eng
Zenodo
https://zenodo.org/communities/eu
https://zenodo.org/communities/alfa
https://doi.org/10.5281/zenodo.3821144
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
INTSYS 2019, 3rd EAI International Conference on Intelligent Transport Systems, Braga, Portugal, 4-6 December 2019
Object Tracking
Deep Learning
Residual Networks
Tracking and Classification of Aerial Objects
info:eu-repo/semantics/conferencePaper