Angelique just sent me the following postdoc announcement:
Adaptive antenna processing correcting the decoherence effects induced by the fluctuations of the propagating media
ContextAt the heart of passive acoustics, gain over ambiant noise is the subjet of numerous contributions. Among the various solutions we can think of (e.g., algorithms working at low signal-to-noise ratios, amplification of the signal of interest...) the technique consisting of using many hydrophones and adding up their contributions has been considered as a simple and efficient way to improve the signal power over the noise. However, it relies on the assumption of phase coherence between the acquired signals. On large antenna, this assumption may be questioned, notably because of the fluctuations in the propagating medium. In practice, if the signal-to-noise ratio may be increased with the length of the antenna, one can observe a stagnancy of the improvement when the length becomes high (higher than a certain threshold, depending both on the frequency and the correlation length characterizing the fluctuations in the medium).
In this project, we aim at developing and validating new adapative antenna processing, correcting the decoherence effects induced by the fluctuations of the propagating medium.
In particular, we will be interested in two tasks:
- calibrating the antenna processing, i.e., estimating the phase offsets along the antenna and the parameters of the propagating medium,
- source localisation.To do this, we envisage methods combining both physical a priori on the acoustic propagation and proper models stemming from signal processing. Among the latter, the sparse representations have proved to be relevant in different applicative domains. Recently considered in passive acoustics, their exploitation has led to promising results.
In continuation of recent studies in propagation in uncertain media, we will adopt a probabilistic point of view. The lack of knowledge or the uncertainties on the parameters of the physical models will be taken into account through Bayesian modeling.Qualifications
Applicants must hold a Ph.D degree in signal processing and substantial expertise in the field of Bayesian modeling and inference. Experience in underwater acoustics would be appreciated. Applicants must be fluent in Matlab.
- Funding: DGA project
- Duration: 1 year (renewable twice)
- Net salary: ~2000€ / month
- Localization: ENSTA Bretagne, Lab-STICC (UMR CNRS 6285), 2 rue Francois Verny, 29806 Brest cedex 9, France
- Start date: from october 2015
All applicants must submit a cover letter, a CV and a publication list. Any other material (e.g., recommendation letter, distinction...) that might strengthen the application is welcome. All materials must be sent by e-mail to Angélique Drémeau (see contact below).
- J. A. Colosi, M. G. Brown – Efficient numerical simulation of stochastic internal-wave induced sound-speed perturbation fields – The Journal of Acoustic Society of America, Vol. 103, no. 4, april
- D. Fattaccioli, X. Cristol, D. F Picard Destelan, and P. Danet – Sonar processing performance in random environments – Proceedings Of Underwater Acoustic Measurements (UAM 09), Nafplion
- A. Drémeau, A. Liutkus, D. Martina, O. Katz, C. Schülke, F. Krzakala, S. Gigan, L. Daudet - Reference-less measurement of the transmission matrix of a highly scattering material using a DMD and phase retrieval techniques - Optics Express, Vol. 23, pp. 11898-11911, 2015.
- S. E. Dosso, M. J. Willmut – Bayesian multiple-source localization in an uncertain ocean environment – The Journal of Acoustic Society of America, Vol. 129, no. 6, june 2011.
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