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Annonce

22 janvier 2018

Machine Learning for Radar Detection and Estimation


Catégorie : Post-doctorant


Department/Dir./Serv. : SONDRA

Place: Centralesupélec, Gif sur Yvette (35 Km from Paris)

Supervisors : Jean-Philippe Ovarlez, Chengfang Ren

Mail : jeanphilippe.ovarlez@centralesupelec.fr ; chengfang.ren@centralesupelec.fr

SONDRA is a Franco-Singaporean research laboratory born from the alliance between Supélec (now CentraleSupélec, French engineering school), ONERA (French national aerospace research center), NUS (National University of Singapore) and DSO National Laboratories. SONDRA was officially launched on April 28, 2004. Based in France on the CentraleSupélec campus, SONDRA carries out fundamental research activities towards integrative research on radar observation combining physics, signal processing and machine learning for the defense, aeronautics and space sectors.

 

Machine Learning for Radar Detection and Estimation

Context

Since the last decade, there is a growing interest for machine and deep learning methods to perform classification and regression tasks. Even though radar target detection and estimation respectively can be turned into a classification and regression problem, radar signals processing are still dominated by physics model based processing techniques [1]. These methods are close to optimal under condition that the collected data belong to a class of well specified distribution [2, 3] (Gaussian, compound Gaussian, elliptically symmetric distribution etc.). The latter point could be questionable since radar unwanted echoes, namely radar clutter, are environment depending. One can argue that Gaussian noise distribution is justified by Central Limit Theorem but this approximation may be only valid for low range resolution radars. For high resolution radar, conventional signal processing assumes noise distribution to be Gaussian scale mixture or elliptically symmetric distributions [4, 5] which are shown to be an efficient assumption for a more robust clutter modeling under presence of outliers. However, these modeling may not capture the true underlying noise distribution (heterogeneity, non-stationarity of the clutter) for a fixed data acquisition environment. On the other hand, machine and deep learning approaches have demonstrated to be very efficient in speech and image recognition and many other areas... Under availability of large amounts of data, machine and deep learning methods could be benefit to process high resolution radar signals [6] under heterogeneous and non-stationary background. Additionally, machine learning can be used for modelling non-linear transformation [7], it might provide a computational efficient methodfor signal processing, and also give improved target detection and parameter estimation. Therefore, we are looking for thought leaders who can develop new areas and applications using machine and deep learning based methods for target detection, estimation and the inverse transform between radar measurement space and interest parameter space.

References

[1] Mark Richards, “Fundamentals of Radar Signal Processing”, McGraw-Hill, 2005.

[2] A. De Maio and M. S. Greco, “Modern Radar Detection Theory”. IET, 2015.

[3] M. Greco, Y. Abramovich, J.-P. Ovarlez, H. Li and X. Yang, ”Introduction to the Issue on Advanced Signal Processing Techniques for Radar Applications”, Selected Topics in Signal Processing,IEEE Journal of, 9(8), pp.1363-1365, 2015.

[4] F. Pascal, Y. Chitour, J.-P. Ovarlez, P. Forster and P. Larzabal, "Covariance Structure Maximum Likelihood Estimates in Compound Gaussian Noise: Existence and Algorithm Analysis", Signal Processing, IEEE Transactions on, 56(1), pp.34-48, Jan. 2008.

[5] F. Pascal, J.-P. Ovarlez, P. Forster and P. Larzabal, "Performance Analysis of Covariance Matrix Estimates in Impulsive Noise", Signal Processing, IEEE Transactions on, 56(6), pp.2206-2217, Jun. 2008.

[6] E. Mason, B. Yonel and B. Yazici, “Deep learning for radar, IEEE Radar Conf., 2017.

[7] A. Mousavi and R. G. Baraniuk. “Learning to invert: Signal recovery via convolutional network”, IEEE ICASSP, pp. 2272 – 2276, 2017.

Mission

The mission of this postdoc consists in:

Candidate profile

Candidates should have:

How to apply

Each applicant should send a CV and a list of his publications.

Applications should be submitted by email to: jeanphilippe.ovarlez@centralesupelec.fr and chengfang.ren@centralesupelec.fr as soon as possible.

Location: SONDRA, campus of Centralesupelec.

Salary (gross): 3000€/month.

Contract duration: 12 months.

The Postdoc will start as soon as possible or at latest June 2018.

 

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