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Raw High-Definition Radar for Multi-Task Learning [article]

Julien Rebut, Arthur Ouaknine, Waqas Malik, Patrick Pérez
<span title="2022-04-13">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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<span title="2022-04-07">2022</span> <span class="release-stage" >pre-print</span>
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Recent progress toward High Definition (HD) Imaging radar has driven the angular resolution below the degree, thus approaching laser scanning performance.  ...  On both tasks, it competes with the most recent radar-based models while requiring less compute and memory.  ...  The used radar is Low-Definition (LD), High-Definition (HD) or Scanning (S).  ... 
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Julien Rebut, Arthur Ouaknine, Waqas Malik, Patrick Pérez. "Raw High-Definition Radar for Multi-Task Learning." arXiv (2022)

An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving [article]

Florian Heidecker, Jasmin Breitenstein, Kevin Rösch, Jonas Löhdefink, Maarten Bieshaar, Christoph Stiller, Tim Fingscheidt, Bernhard Sick
<span title="2021-03-05">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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Systems and functions that rely on machine learning (ML) are the basis of highly automated driving.  ...  A complication for the development of corner case detectors is the lack of consistent definitions, terms, and corner case descriptions, especially when taking into account various automotive sensors.  ...  ACKNOWLEDGMENT This work results from the project KI Data Tooling (19A20001O) funded by German Federal Ministry for Economic Affairs and Energy (BMWI) and the DeCoInt 2 -project financed by the German  ... 
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Florian Heidecker, Jasmin Breitenstein, Kevin Rösch, Jonas Löhdefink, Maarten Bieshaar, Christoph Stiller, Tim Fingscheidt, Bernhard Sick. "An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving." arXiv (2021)

LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion [article]

Meet Shah, Zhiling Huang, Ankit Laddha, Matthew Langford, Blake Barber, Sidney Zhang, Carlos Vallespi-Gonzalez, Raquel Urtasun
<span title="2020-11-12">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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In this paper, we present LiRaNet, a novel end-to-end trajectory prediction method which utilizes radar sensor information along with widely used lidar and high definition (HD) maps.  ...  radar information, we show a 52% reduction in prediction error for objects with high acceleration and a 16% reduction in prediction error for objects at longer range.  ...  For objects with a high number of radar points, it may be possible to learn this from a single sweep. On objects with sparse radar points, however, these observations have to come from past frames.  ... 
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Meet Shah, Zhiling Huang, Ankit Laddha, Matthew Langford, Blake Barber, Sidney Zhang, Carlos Vallespi-Gonzalez, Raquel Urtasun. "LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion." arXiv (2020)

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

Stamatios Samaras, Eleni Diamantidou, Dimitrios Ataloglou, Nikos Sakellariou, Anastasios Vafeiadis, Vasilis Magoulianitis, Antonios Lalas, Anastasios Dimou, Dimitrios Zarpalas, Konstantinos Votis, Petros Daras, Dimitrios Tzovaras
<span title="2019-11-06">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;

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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  ...  Therefore, the need to present a complete overview of deep learning technologies applied to c-UAV related tasks on multi-sensor data has emerged.  ...  Utilizing deep learning techniques for multi-sensor learning tasks displays major benefits.  ... 
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<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200212011659/https://res.mdpi.com/d_attachment/sensors/sensors-19-04837/article_deploy/sensors-19-04837-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e3/49/e349647588b72e00f5b5d2cc86a915fd1a8e6c06.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s19224837"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891421" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>
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Stamatios Samaras, Eleni Diamantidou, Dimitrios Ataloglou, Nikos Sakellariou, Anastasios Vafeiadis, Vasilis Magoulianitis, Antonios Lalas, Anastasios Dimou, Dimitrios Zarpalas, Konstantinos Votis, Petros Daras, Dimitrios Tzovaras. "Deep Learning on Multi Sensor Data for Counter UAV Applications—A Systematic Review." Sensors 19.22 (2019) 4837

Deep learning for radar data exploitation of autonomous vehicle [article]

Arthur Ouaknine
<span title="2022-03-15">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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Finally, this thesis exposes a collaborative contribution, the RADIal dataset with synchronised High-Definition (HD) RADAR, LiDAR and camera.  ...  The RADAR sensor is seldom used for scene understanding due to its poor angular resolution, the size, noise, and complexity of RADAR raw data as well as the lack of available datasets.  ...  I would like to especially thank Domique Béréziat and Francesca Bovolo for reviewing my manuscript.  ... 
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Citation

Arthur Ouaknine. "Deep learning for radar data exploitation of autonomous vehicle." arXiv (2022)

RADAR 2019 Author Index

<span title="">2019</span> <i title="IEEE"> 2019 International Radar Conference (RADAR) </i> &nbsp;

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Variable Structure Filter for Radar Target Tracking submission_191 HE Yuan BiGRU Network for Human Activity Recognition in High Resolution Range Profile submission_97 HE Yuan A Deep Multi-task  ...  for Human Activity Recognition in High Resolution Range Profile submission_97 JING Xiaojun A Deep Multi-task Network for Activity Recognition and Person Identification with Micro-Doppler Signatures  ... 
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"RADAR 2019 Author Index." 2019 International Radar Conference (RADAR) (2019)

One Size Does Not Fit All: Multi-Scale, Cascaded RNNs for Radar Classification [article]

Dhrubojyoti Roy, Sangeeta Srivastava, Aditya Kusupati, Pranshu Jain, Manik Varma, Anish Arora
<span title="2019-09-06">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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Existing solutions for the clutter versus multi-source radar classification task are limited in terms of either accuracy or efficiency, and in some cases, struggle with a trade-off between false alarms  ...  We propose a multi-scale, cascaded recurrent neural network architecture, MSC-RNN, comprised of an efficient multi-instance learning (MIL) Recurrent Neural Network (RNN) for clutter discrimination at a  ...  Learning on Constrained Devices 2 .  ... 
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Dhrubojyoti Roy, Sangeeta Srivastava, Aditya Kusupati, Pranshu Jain, Manik Varma, Anish Arora. "One Size Does Not Fit All: Multi-Scale, Cascaded RNNs for Radar Classification." arXiv (2019)

JMRPE‐Net: Joint modulation recognition and parameter estimation of cognitive radar signals with a deep multitask network

Mengtao Zhu, Ziwei Zhang, Cong Li, Yunjie Li
<span title="2021-06-17">2021</span> <i title="Institution of Engineering and Technology (IET)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/diaq33bq2jexne77uyugzdv2g4" style="color: black;">IET radar, sonar &amp; navigation</a> </i> &nbsp;

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The newly developed cognitive radar (CR) can implement flexible work modes defined with a set of mode definition parameters.  ...  The proposed network can receive a sequence of CR pulse signals as input and parallelly perform automatic modulation recognition (AMR) and modulation parameter estimation tasks for multiple work mode definition  ...  Also, the authors would like to appreciate the editors and anonymous reviewers for their efforts and constructive comments that helped to improve the quality of this article.  ... 
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<span class="ui label basic orange small" title="Directory of Open Access Journals">DOAJ</span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210715010200/https://ietresearch.onlinelibrary.wiley.com/doi/pdfdirect/10.1049/rsn2.12142" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5a/b1/5ab10b7d6d4470a7e178ffe0e776ce53643ac140.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1049/rsn2.12142"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>
Citation

Mengtao Zhu, Ziwei Zhang, Cong Li, Yunjie Li. "JMRPE‐Net: Joint modulation recognition and parameter estimation of cognitive radar signals with a deep multitask network." IET radar, sonar & navigation 15.11 (2021)

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing [article]

Ping Lang, Xiongjun Fu, Marco Martorella, Jian Dong, Rui Qin, Xianpeng Meng, Min Xie
<span title="2020-09-29">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems.  ...  Modern radar systems have high requirements in terms of accuracy, robustness and real-time capability when operating on increasingly complex electromagnetic environments.  ...  of multi-task learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2009.13702v1">arXiv:2009.13702v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/m6am73324zdwba736sn3vmph3i">fatcat:m6am73324zdwba736sn3vmph3i</a> </span>
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Citation

Ping Lang, Xiongjun Fu, Marco Martorella, Jian Dong, Rui Qin, Xianpeng Meng, Min Xie. "A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing." arXiv (2020)

Weakly Supervised Deep Learning Method for Vulnerable Road User Detection in FMCW Radar

Martin Dimitrievski, Ivana Shopovska, David Van Hamme, Peter Veelaert, Wilfried Philips
<span title="2020-09-20">2020</span> <i title="IEEE"> 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) </i> &nbsp;

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The main contribution is a weakly supervised training method which uses abundant, automatically generated labels from camera and lidar for training the model.  ...  Millimeter-wave radar is currently the most effective automotive sensor capable of all-weather perception.  ...  This task is especially difficult when labeling raw radar data which is non-intuitive to the untrained eye.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/itsc45102.2020.9294399">doi:10.1109/itsc45102.2020.9294399</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vvlhnrysjjec3aajff2cb62fce">fatcat:vvlhnrysjjec3aajff2cb62fce</a> </span>
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Citation

Martin Dimitrievski, Ivana Shopovska, David Van Hamme, Peter Veelaert, Wilfried Philips. "Weakly Supervised Deep Learning Method for Vulnerable Road User Detection in FMCW Radar." 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) (2020)

Keynote Speeches

<span title="">2019</span> <i title="IEEE"> 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP) </i> &nbsp;

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Recently, deep learning techniques are being actively developed worldwide for tomographic image reconstruction.  ...  He is Lead Guest Editor of five IEEE Abstract: With the development of remote sensing technology, the Earth observation satellites have the characteristics of high resolution, wide coverage and multi-satellite  ...  This talk includes several parts on person ReID:  Task definition, challenges and benchmarks of person ReID  Fully supervised learning for person ReID  Unsupervised learning or weakly supervised learning  ... 
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"Keynote Speeches." 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP) (2019)

Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems [article]

Hannes Reichert, Lukas Lang, Kevin Rösch, Daniel Bogdoll, Konrad Doll, Bernhard Sick, Hans-Christian Reuss, Christoph Stiller, J. Marius Zöllner
<span title="2021-09-28">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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<span title="2021-05-14">2021</span> <span class="release-stage" >pre-print</span> <span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2105.06896v1">arXiv:2105.06896v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5rne5uq27vg7fm63clvetf2254">fatcat:5rne5uq27vg7fm63clvetf2254</a> </span>
We envision sensor data abstraction as an interface between sensor data and machine learning applications for highly automated vehicles (HAD).  ...  For this purpose, we review the primary sensor modalities, camera, lidar, and radar, published in autonomous-driving related datasets, examine single sensor abstraction and abstraction of sensor setups  ...  Further research for multi-modal approaches is necessary. D.  ... 
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Hannes Reichert, Lukas Lang, Kevin Rösch, Daniel Bogdoll, Konrad Doll, Bernhard Sick, Hans-Christian Reuss, Christoph Stiller, J. Marius Zöllner. "Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems." arXiv (2021)

RADAR 2019 Subject Index Page

<span title="">2019</span> <i title="IEEE"> 2019 International Radar Conference (RADAR) </i> &nbsp;

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156 Dictionary Learning for Radar Classification of Multiple Micro-Drones Wenyu Zhang, Gang Li, Chris Baker 188 Effective Ground-Truthing of Supervised Machine Learning for Drone Classification Jacob  ...  Baker Session 22 AI-based Human sensing(special session -Yan Hue) 103 A Deep Multi-task Network for Activity Recognition and Person Identification with Micro-Doppler Signatures Xinyu Li, Yuan He, Xiaojun  ... 
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"RADAR 2019 Subject Index Page." 2019 International Radar Conference (RADAR) (2019)

Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning [article]

Huan Yin, Xuecheng Xu, Yue Wang, Rong Xiong
<span title="2021-01-30">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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<span title="2021-06-18">2021</span> <span class="release-stage">accepted</span> <span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2102.04960v2">arXiv:2102.04960v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3ueopssn4nd4hfdrap5eumr7eq">fatcat:3ueopssn4nd4hfdrap5eumr7eq</a> </span>
To achieve this, a deep neural network is built with joint training in the learning stage, and then in the testing stage, shared embeddings of radar and lidar are extracted for heterogeneous place recognition  ...  The experimental results indicate that our model is able to perform multiple place recognitions: lidar-to-lidar, radar-to-radar and radar-to-lidar, while the learned model is trained only once.  ...  One step further, given that large-scale high-definition lidar maps have been deployed for commercial use Li et al. (2017) , a radar-to-lidar based place recognition is a feasible solution, which is robust  ... 
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Citation

Huan Yin, Xuecheng Xu, Yue Wang, Rong Xiong. "Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning." arXiv (2021)

RADAR 2019 TOC

<span title="">2019</span> <i title="IEEE"> 2019 International Radar Conference (RADAR) </i> &nbsp;

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Cross Section of Naval Targets Yannick Béniguel, Philippe Pouliguen, Gildas Kubicke submission_288 p. 611 Session 22 AI-based Human sensing(special session -Yan Hue) 103 A Deep Multi-task Network  ...  Radar Target Tracking Yanwen Li, You He, Gang Li, Yu Liu submission_191 p. 707 16 Stochastic Deep Learning for Compressive-sensing Radar Radmila Pribić 27 A Ship Target Detection Method for SAR Image  ... 
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"RADAR 2019 TOC." 2019 International Radar Conference (RADAR) (2019)

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