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Mark Howell

Also published under: M. Howell

Publications

Citations

Publications by Year

20052014

Co-Authors:

D. AreniusS. AssadiI. CampisiF. CasagrandeM. Champion

Show All Co-Authors (49)

Mark Howell

Also published under: M. Howell

Affiliation

Trimble Navigation, Christchurch, New Zealand

General Motors Global Research and Development

Warren

MI, USA

Publication Topics

Data Reduction,
Data-driven Process,
Environmentally Friendly Technology,
Fault Diagnosis,
Hybrid Electric Vehicles,
Nominal State,
Nominal Value,
Regeneration System,
Regenerative Braking,
Regenerative Braking System,
Support Vector Machine,
Vector Core

Biography

Mark Howell (M’02–SM’14) received the B.Sc. degree in cybernetics from the University of Reading, Reading, U.K., in 1987, and the M.Sc. and Ph.D. degrees in control engineering from the University of Sheffield, Sheffield, U.K., in 1995. From 1995 to 2003, he was a Post-Doctoral Research Fellow of Automotive Engineering at Loughborough University, Loughborough, U.K. He was a Senior Researcher with General Motors, Detroit, MI, USA, from 2003 to 2014, where he was involved in developing control, prognostic, and vehicle health management solutions for the automotive industry. He is currently a Control Systems Research Engineer with Trimble Navigation, Christchurch, New Zealand, developing autonomous machine control systems. He has authored over...

Publications

Citations

Publications by Year

20052014

Co-Authors:

D. Arenius
S. Assadi
I. Campisi
F. Casagrande
M. Champion

Show All Co-Authors (49)

Author's Published Works

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Showing 1-10 of 10 results

Conferences (7)

Journals (3)

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Eric Mason(9)
Birsen Yazici(7)
Bariscan Yonel(6)
Yonina C. Eldar(2)
Il-Young Son(2)
Tactical Electronic Warfare Division, Naval Research Laboratory, Washington, DC, USA(3)
Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA(2)
Department of Mathematics and Computer Science, Weizmann Institute of Science, Rehovot, Israel(2)
Department of Electrical Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA(2)
United States Naval Research Laboratory, Washington, DC, USA(2)
2018 IEEE Radar Conference (RadarConf18)(2)
IEEE Journal of Selected Topics in Signal Processing(2)
2015 IEEE Radar Conference (RadarCon)(1)
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(1)
2017 IEEE Radar Conference (RadarConf)(1)
IEEE(10)
Oklahoma City, OK, USA(2)
Arlington, VA, USA(1)
Atlanta, GA, USA(1)
New Orleans, LA, USA(1)
Pacific Grove, CA, USA(1)
Synthetic Aperture Radar(9)
Synthetic Aperture Radar Images(9)
Passive Synthetic Aperture Radar(6)
Forward Model(5)
Numerical Simulations(5)

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Phase-Space Function Recovery for Moving Target Imaging in SAR by Convex Optimization

Sean Thammakhoune;Bariscan Yonel;Eric Mason;Birsen Yazici;Yonina C. Eldar

IEEE Transactions on Computational Imaging

Year: 2021 | Volume: 7 | Journal Article |

Cited by: Papers (3)

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In this paper, we present an approach for ground moving target imaging (GMTI) and velocity recovery using synthetic aperture radar. We formulate the GMTI problem as the recovery of a phase-space reflectivity (PSR) function which represents the strengths and velocities of the scatterers in a scene of interest. We show that the discretized PSR matrix can be decomposed into a rank-one, and a highly s...Show More

Phase-Space Function Recovery for Moving Target Imaging in SAR by Convex Optimization

Sean Thammakhoune;Bariscan Yonel;Eric Mason;Birsen Yazici;Yonina C. Eldar

IEEE Transactions on Computational Imaging

Year: 2021 | Volume: 7 | Journal Article |

Moving Target Imaging for Synthetic Aperture Radar Via RPCA

Sean Thammakhoune;Bariscan Yonel;Eric Mason;Birsen Yazici;Yonina C. Eldar

2021 IEEE Radar Conference (RadarConf21)

Year: 2021 | Conference Paper |

Cited by: Papers (7)

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Synthetic aperture radar (SAR) imaging of moving targets is a challenging task, as standard techniques have been developed for stationary scenes. Motivated by success of robust principal component analysis (RPCA) in change detection for video processing, we establish a rank-1 and sparse decomposition framework for the SAR problem in the image domain. We construct the phase-space reflectivity matri...Show More

Moving Target Imaging for Synthetic Aperture Radar Via RPCA

Sean Thammakhoune;Bariscan Yonel;Eric Mason;Birsen Yazici;Yonina C. Eldar

2021 IEEE Radar Conference (RadarConf21)

Year: 2021 | Conference Paper |

Phaseless Passive Synthetic Aperture Radar Imaging via Wirtinger Flow

Bariscan Yonel;Eric Mason;Birsen Yazici

2018 52nd Asilomar Conference on Signals, Systems, and Computers

Year: 2018 | Conference Paper |

Cited by: Papers (7)

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In this paper, we present a novel method for passive imaging of stationary targets using phaseless measurements. One of the main benefits of the proposed approach is increased robustness to uncertainties inherent in passive imaging. Furthermore, unlike most passive radar imaging methods, our approach circumvents the need for two receivers by auto-correlating the received bistatic signal. Since the...Show More

Phaseless Passive Synthetic Aperture Radar Imaging via Wirtinger Flow

Bariscan Yonel;Eric Mason;Birsen Yazici

2018 52nd Asilomar Conference on Signals, Systems, and Computers

Year: 2018 | Conference Paper |

Deep learning for waveform estimation in passive synthetic aperture radar

Bariscan Yonel;Eric Mason;Birsen Yazici

2018 IEEE Radar Conference (RadarConf18)

Year: 2018 | Conference Paper |

Cited by: Papers (3)

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We propose Deep Learning (DL) as a framework for simultaneous waveform estimation and image reconstruction in passive synthetic aperture radar (SAR). We consider a passive imaging scenario where the scene is illuminated by source of opportunity with known position, but transmitting an unknown waveform. Image reconstruction is then posed as a machine learning task for which a recurrent neural netwo...Show More

Deep learning for waveform estimation in passive synthetic aperture radar

Bariscan Yonel;Eric Mason;Birsen Yazici

2018 IEEE Radar Conference (RadarConf18)

Year: 2018 | Conference Paper |

Passive phaseless SAR imaging

Eric Mason;Birsen Yazici

2018 IEEE Radar Conference (RadarConf18)

Year: 2018 | Conference Paper |

Cited by: Papers (5)

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Passive synthetic aperture radar (SAR) is a coherent imaging method. Thus, performance is very susceptible to phase error, which result because the transmitter is not under control of the user. These errors result from poor clock synchronization, uncertainty in transmitter location and trajectory error, to name a few. In this paper we present an incoherent imaging method for passive SAR which uses...Show More

Passive phaseless SAR imaging

Eric Mason;Birsen Yazici

2018 IEEE Radar Conference (RadarConf18)

Year: 2018 | Conference Paper |

Deep Learning for Passive Synthetic Aperture Radar

Bariscan Yonel;Eric Mason;Birsen Yazıcı

IEEE Journal of Selected Topics in Signal Processing

Year: 2018 | Volume: 12, Issue: 1 | Journal Article |

Cited by: Papers (48)

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We introduce a deep learning (DL) framework for inverse problems in imaging, and demonstrate the advantages and applicability of this approach in passive synthetic aperture radar (SAR) image reconstruction. We interpret image reconstruction as a machine learning task and utilize deep networks as forward and inverse solvers for imaging. Specifically, we design a recurrent neural network (RNN) archi...Show More

Deep Learning for Passive Synthetic Aperture Radar

Bariscan Yonel;Eric Mason;Birsen Yazıcı

IEEE Journal of Selected Topics in Signal Processing

Year: 2018 | Volume: 12, Issue: 1 | Journal Article |

A weakly-convex formulation for phaseless imaging

İ. Bayram;E. Mason;B. Yazıcı

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2017 | Conference Paper |

Cited by: Papers (3)

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We consider the problem of reconstructing an object given magnitudes of linear measurements. We follow the `lifting' approach, but unlike previous work which use convex relaxations of the unit rank constraint, we use a weakly-convex matrix penalty. We derive a convergent algorithm and show that it is computationally more feasible than those obtained under convex relaxations. We demonstrate numeric...Show More

A weakly-convex formulation for phaseless imaging

İ. Bayram;E. Mason;B. Yazıcı

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2017 | Conference Paper |

Deep learning for radar

Eric Mason;Bariscan Yonel;Birsen Yazici

2017 IEEE Radar Conference (RadarConf)

Year: 2017 | Conference Paper |

Cited by: Papers (55) | Patents (1)

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Motivated by the recent advances in deep learning, we lay out a vision of how deep learning techniques can be used in radar. Specifically, our discussion focuses on the use of deep learning to advance the state-of-the-art in radar imaging. While deep learning can be directly applied to automatic target recognition (ATR), the relevance of these techniques in other radar problems is not obvious. We ...Show More

Deep learning for radar

Eric Mason;Bariscan Yonel;Birsen Yazici

2017 IEEE Radar Conference (RadarConf)

Year: 2017 | Conference Paper |

Passive Synthetic Aperture Radar Imaging Using Low-Rank Matrix Recovery Methods

Eric Mason;Il-Young Son;Birsen Yazıcı

IEEE Journal of Selected Topics in Signal Processing

Year: 2015 | Volume: 9, Issue: 8 | Journal Article |

Cited by: Papers (35)

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We present a novel image formation method for passive synthetic aperture radar (SAR) imaging. The method is an alternative to widely used time difference of arrival (TDOA) or correlation-based backprojection method. These methods work under the assumption that the scene is composed of a single or a few widely separated point targets. The new method overcomes this limitation and can reconstruct het...Show More

Passive Synthetic Aperture Radar Imaging Using Low-Rank Matrix Recovery Methods

Eric Mason;Il-Young Son;Birsen Yazıcı

IEEE Journal of Selected Topics in Signal Processing

Year: 2015 | Volume: 9, Issue: 8 | Journal Article |

Passive synthetic aperture radar imaging based on low-rank matrix recovery

Eric Mason;Il-Young Son;Birsen Yazici

2015 IEEE Radar Conference (RadarCon)

Year: 2015 | Conference Paper |

Cited by: Papers (7)

HTML

We present a novel image formation method for passive synthetic aperture radar (SAR) imaging. The method is an alternative to Time Difference of Arrival (TDOA) based backprojection method. The TDOA based backprojection works under the assumption that the scene is composed of a single or a few widely separated point targets. The new method overcomes this limitation and can reconstruct heterogenous ...Show More

Passive synthetic aperture radar imaging based on low-rank matrix recovery

Eric Mason;Il-Young Son;Birsen Yazici

2015 IEEE Radar Conference (RadarCon)

Year: 2015 | Conference Paper |

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Author details

Mark Howell

Mark Howell

Affiliation

Publication Topics

Biography

Showing 1-10 of 10 results

Show

Phase-Space Function Recovery for Moving Target Imaging in SAR by Convex Optimization

Phase-Space Function Recovery for Moving Target Imaging in SAR by Convex Optimization

Moving Target Imaging for Synthetic Aperture Radar Via RPCA

Moving Target Imaging for Synthetic Aperture Radar Via RPCA

Phaseless Passive Synthetic Aperture Radar Imaging via Wirtinger Flow

Phaseless Passive Synthetic Aperture Radar Imaging via Wirtinger Flow

Deep learning for waveform estimation in passive synthetic aperture radar

Deep learning for waveform estimation in passive synthetic aperture radar

Passive phaseless SAR imaging

Passive phaseless SAR imaging

Deep Learning for Passive Synthetic Aperture Radar

Deep Learning for Passive Synthetic Aperture Radar

A weakly-convex formulation for phaseless imaging

A weakly-convex formulation for phaseless imaging

Deep learning for radar

Deep learning for radar

Passive Synthetic Aperture Radar Imaging Using Low-Rank Matrix Recovery Methods

Passive Synthetic Aperture Radar Imaging Using Low-Rank Matrix Recovery Methods

Passive synthetic aperture radar imaging based on low-rank matrix recovery

Passive synthetic aperture radar imaging based on low-rank matrix recovery

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