Staff

M. Sc. Anand Dubey

Contact

  • Mail:
  • Phone: 09131/85-27190
  • Fax: 09131/85-28730
  • Room: 04.236
  • Cauerstraße 9
    91058 Erlangen

About Anand Dubey

Biography

In February, 2018 Anand Dubey finished his Master's Degree in Automotive Software Engineering at the Technical  University of Chemnitz. Since September 2018 he is working as a research assistant at the Institute for Electronics Engineering as part of the team's Circuits, Systems & Hardware Test (CST). He has worked for 3.5 years in Automotive Industry in the area of embedded programming, computer vision and machine learning. Currently, he is working for Prystine project. 

Areas of Interest

  • Radar Signal Processing
  • Machine Learning Algorithms

Open thesis projetcs

Just call me!

Publications

2020

  • M. Lübke, J. Fuchs, V. Shatov, A. Dubey, R. Weigel, and F. Lurz, "Simulation Environment of a Communication System Using CDMA at 77 GHz" in Wireless Communications & Mobile Computing (IWCMC 2020), Limassol, Cyprus, 2020 (to be published). [Bibtex]
    @inproceedings{luebke2020a,
    abstract = {In this paper, an overall concept for a joint communication-sensing system at 77 GHz is presented with special focus on the communication part.To take advantage of the reduced interference between vehicles, code division multiplexing using direct sequence spread spectrum signals is applied. The system, which covers the whole signal processing chain, is introduced, explained and simulated using Simulink/Matlab software. A design, capable of spreading, modulating, including non-idealities of radio frequency-blocks and synchronization, is built up and evaluated. Additionally, a channel model is simulated in the software WinProp and integrated in the Simulink simulation. In consequence, a more realistic model compared to an estimation-based Rician-or Rayleigh channel model is realized. Furthermore, different modulation schemes, binary phase shift keying, 4- and 16-quadrature amplitude modulation, are investigated. The system is rated with respect to the bit-error-rate by applying additive white Gaussian noise. The functionality is verified as the the results match with the theoretical assumptions The system is further improved by building up a Rake-Receiver structure.},
    author = {Lübke, Maximilian and Fuchs, Jonas and Shatov, Victor and Dubey, Anand and Weigel, Robert and Lurz, Fabian},
    booktitle = {Wireless Communications & Mobile Computing (IWCMC 2020)},
    cris = {https://cris.fau.de/converis/publicweb/publication/236628043},
    year = {2020},
    month = {06},
    day = {15},
    eventdate = {2020-06-15/2020-06-19},
    faupublication = {yes},
    keywords = {77 GHz; joint sensing-communications; direct spread spectrum communication; rake demodulator},
    note = {unpublished},
    peerreviewed = {automatic},
    title = {Simulation Environment of a Communication System Using CDMA at 77 GHz},
    venue = {Limassol, Cyprus},
    }
  • A. Dubey, J. Fuchs, M. Lübke, R. Weigel, and F. Lurz, "Generative Adversial Network based Extended Target Detection for Automotive MIMO Radar" in 2018 International Conference on Radar (RADAR), Washington DC, USA, 2020 (to be published). [Bibtex]
    @inproceedings{dubey2020,
    abstract = {In recent years, the automotive radar systems has gained substantial interest for different applications of autonomous driving. The performance of most applications likes classification and tracking directly relies on accurate target detection. The state-of-the-art detection pipeline is vulnerable to multi-path reflections, clutter noise, interference from another radar and leads to false or ghost detections. To address this issue, an end-to-end target detection pipeline using a residual based U-Net architecture is proposed. In contrast to the conventional approach, the network directly generates the detection map from range-Doppler map. The network uses a generative adversarial training over multiple real world measurements. We demonstrate that the proposed network can learn effectively to detect extended targets and shows significant improvement under increased noise floor in comparison to the state-of-the-art detection techniques.},
    author = {Dubey, Anand and Fuchs, Jonas and Lübke, Maximilian and Weigel, Robert and Lurz, Fabian},
    language = {English},
    booktitle = {2018 International Conference on Radar (RADAR)},
    cris = {https://cris.fau.de/converis/publicweb/publication/231325915},
    year = {2020},
    month = {04},
    day = {27},
    eventdate = {2020-04-27/2020-05-01},
    faupublication = {yes},
    note = {unpublished},
    peerreviewed = {automatic},
    title = {Generative Adversial Network based Extended Target Detection for Automotive MIMO Radar},
    venue = {Washington DC, USA},
    }
  • J. Fuchs, A. Dubey, M. Lübke, R. Weigel, and F. Lurz, "Automotive Radar Interference Mitigation using a Convolutional Autoencoder" in 2020 International Conference on Radar (RADAR), Washington DC, USA, 2020 (to be published). [Bibtex]
    @inproceedings{fuchs2020,
    abstract = {Automotive radar interference imposes big challenges on signal processing algorithms as it raises the noise floor and consequently lowers the detection probability. With limited frequency bands and increasing number of sensors per car, avoidance techniques such as frequency hopping or beamforming quickly become insufficient. Detect-and-repair strategies have been studied intensively for the automotive field, to reconstruct the affected signal samples. However depending on the type of interference, reconstruction of the time domain signals is a highly non-trivial task, which can affect following signal processing modules. In this work an autoencoder based convolutional neural network is proposed to perform image based denoising. Interference mitigation is phrased as a denoising task directly on the range-Doppler spectrum. The neural networks shows significant improvement with respect to signal-to-noise-plus-interference ratio in comparison to other state-of-the-art mitigation techniques, while better preserving phase information of the spectrum compared to other techniques.},
    author = {Fuchs, Jonas and Dubey, Anand and Lübke, Maximilian and Weigel, Robert and Lurz, Fabian},
    language = {English},
    booktitle = {2020 International Conference on Radar (RADAR)},
    cris = {https://cris.fau.de/converis/publicweb/publication/231966704},
    year = {2020},
    month = {04},
    day = {27},
    eventdate = {2020-04-27/2020-05-01},
    faupublication = {yes},
    note = {unpublished},
    peerreviewed = {unknown},
    title = {Automotive Radar Interference Mitigation using a Convolutional Autoencoder},
    type = {Konferenzschrift},
    venue = {Washington DC, USA},
    }
  • M. Lübke, J. Fuchs, V. Shatov, A. Dubey, R. Weigel, and F. Lurz, "Combining Radar and Communication at 77 GHz Using a CDMA Technique" in 2020 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM), Linz, Austria, 2020 (to be published). [Bibtex]
    @inproceedings{luebke2020,
    author = {Lübke, Maximilian and Fuchs, Jonas and Shatov, Victor and Dubey, Anand and Weigel, Robert and Lurz, Fabian},
    booktitle = {2020 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM)},
    cris = {https://cris.fau.de/converis/publicweb/publication/234501926},
    year = {2020},
    month = {04},
    day = {20},
    eventdate = {2020-04-20/2020-04-22},
    faupublication = {yes},
    note = {unpublished},
    peerreviewed = {automatic},
    title = {Combining Radar and Communication at 77 GHz Using a CDMA Technique},
    venue = {Linz, Austria},
    }
  • A. Dubey, J. Fuchs, T. Reißland, R. Weigel, and F. Lurz, "Uncertainty Analysis of Deep Neural Network for Classification of Vulnerable Road Users using micro-Doppler" in IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet), San Antonio, Texas, USA, 2020 (to be published). [Bibtex]
    @inproceedings{dubey2020a,
    abstract = {Unlike optical imaging, it’s difficult to extract descriptive features from radar data for problems like classification of different targets. This paper takes the advantage of different neural network based architectures such as convolutional neural networks and long-short term memory to propose an end-to-end framework for classification of vulnerable road users. To make the network’s prediction more reliable for automotive applications, a new concept of network uncertainty is introduced to the defined architectures. The signal processing tool chain described in this paper achieves higher accuracy than state-of-the-art algorithms while maintaining latency requirement for automotive applications.},
    author = {Dubey, Anand and Fuchs, Jonas and Reißland, Torsten and Weigel, Robert and Lurz, Fabian},
    language = {English},
    booktitle = {IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet)},
    cris = {https://cris.fau.de/converis/publicweb/publication/229613648},
    year = {2020},
    month = {01},
    day = {26},
    eventdate = {2020-01-26/2020-01-29},
    faupublication = {yes},
    keywords = {Autonomous Driving,Automotive Radar,Deep Neural Networks,Mico-Doppler,VRU Classification.},
    note = {unpublished},
    peerreviewed = {automatic},
    title = {Uncertainty Analysis of Deep Neural Network for Classification of Vulnerable Road Users using micro-Doppler},
    venue = {San Antonio, Texas, USA},
    }

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