Prof. Dr. Ali Khenchaf
Lab-STICC, UMR CNRS 6285, ENSTA Bretagne, France
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Academic Career Length
31Years (1996-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications29 October 2007 in IEEE Transactions on Geoscience and Remote Sensing
Journal Article
We describe in detail the theoretical and practical implementation aspects of a simulation for marine radars which can, in particular, be used in multistatic configurations. Since the simulator is intended to deliver pseudoraw signals, it can be used later as a tool to benchmark and improve postprocessing algorithms such as bistatic synthetic aperture radar focusing algorithms and ship wake detection algorithms. The work is divided into two parts. This paper reviews and recalls theoretical prerequisites necessary in implementing such a simulator. Included are the full derivation of the bistatic radar equation from the transmitter to the receiver, accounting also for the transmit-receive time, a description of the sea state phenomenology, a review of the theory of electromagnetic scattering from the sea surface, and the presentation and validation of the method used in the simulation. A companion paper discusses the practical implementation aspects of the simulator as well as an analysis of our results.
15 July 2008 in IEEE Transactions on Aerospace and Electronic Systems
Journal Article
A generalized likelihood ratio test (GLRT) is derived for adaptive detection of range and Doppler-distributed targets. The clutter is modeled as a spherically invariant random process (SIRP) and its texture component is range dependent (heterogeneous clutter). We suppose here that the speckle component covariance matrix is known or estimated thanks to a secondary data set. Thus, unknown parameters to be estimated are local texture values, the complex amplitudes and Doppler frequencies of all scattering centers. To do so, we use superresolution methods. The proposed detector assumes a priori knowledge on the spatial distribution of the target and has the precious property of having a constant false alarm rate (CFAR) with the assumption of a known speckle covariance matrix or by the use of frequency agility.
1 April 2001 in Waves in Random Media
Journal Article
Ali KhenchafThe scattering of waves by random rough surfaces has important applications in the remote sensing of oceans and land. The problem of developing a model for rough surfaces is very difficult since, at best, the scattering coefficient σ0 is dependent upon (at least) the radar frequency, geometrical and physical parameters, incident and observation angles, and polarization. The problem of electromagnetic scattering from a randomly rough surface is analysed using the Kirchhoff approximation (stationary phase, scalar approximation), the small-perturbation model and the two-scale models. A first major new consideration in this paper is the polarimetric signature calculations as a function of the transmitter location and receiver location for a bistatic radio-link. We calculate the like- and cross-polarized received power directly using the scattering coefficients, without calculating the Mueller matrix. Next, a study of the regions of validity of the Kirchhoff and small-perturbation rough surface scatte...
1 August 2006 in Waves in Random and Complex Media
Journal Article
The first-order small-slope approximation (SSA-1) model is used for numerical predictions of the normalized radar cross section (NRCS) of an anisotropic ocean surface in bistatic configurations for the K u -band radar frequency. The calculations were made by assuming the Elfouhaily et al. surface-height spectrum for fully developed seas. In the forward–backward case, the SSA-1 presents an agreement with the geometric optics limit of the Kirchhoff approximation results in the near-specular directions where it is well known that the last model works well. In the fully bistatic case, SSA-1 numerical results are compared with those of the two-scale model in several configurations as a function of wind speed, wind direction, incident/scattering angles and for co-and cross-polarization states. Good agreement between the two models is noted in the co-and cross-polarization case with a small difference of about 1–2 dB. But in certain configurations, the SSA-1 model tends to overestimate the radar cross section peak behaviour. This irregularity is discussed and interpreted. The main purpose of this paper is to analyse NRCS predictions based on the SSA-1 model in a fully bistatic configuration.
18 May 2010 in IEEE Transactions on Geoscience and Remote Sensing
Journal Article
High-frequency surface-wave (HFSW) radars are usually used to remotely measure oceanographic parameters. These systems can also potentially detect targets beyond the conventional microwave radar coverage. In this paper, the backscattered Doppler spectrum made up of the sea clutter, ship echoes, and the background noise has been modeled. Taking into account the propagation and the signal-processing effects, a range-Doppler image has been generated. This model can be used for different purposes like the (theoretical) evaluation of detection performance. This paper gives an overview of the theoretical elements for modeling the backscatter signal. The processing effects on the range-Doppler image and the time-evolving target signature are also introduced. Some of the simulated elements and the obtained range-Doppler images are compared with real data. Finally, from this model, the detection capabilities of HFSW radars are evaluated.
1 January 2017
Conference Paper
This paper deals with the problematic of automatic target recognition (ATR) using Synthetic Aperture Radar (SAR) images. In this work, the Deep Learning (DL) architecture is proposed and applied in order to recognize military vehicles from SAR images. We propose mainly in this work the deep learning algorithms based on convolutional neural network architecture. In the second step and in order to optimize the convolution of DL steps, we propose to use a convolutional auto-encoder which may be better suited to image processing. Its use provides several areas of the best results in the presence of noise on shifted and truncated images. To validate our approach, some experimentation results are given and compared. The obtained results show that the proposed approach of DL achieves a height recognition accuracy of 93%.
About Me
Ali Khenchaf (Member Senior, IEEE) received his PhD degree from the University of Nantes, France in 1992. From 1993, Dr Khenchaf was a lecturer at the Ecole Polytechnique of the University of Nantes and a researcher, first at the SEI laboratory and then at the IRCCyN laboratory (UMR CNRS 6597). In 2000, he obtained his Habilitation à Diriger les Recherches.
He is currently a Full Professor at ENSTA Bretagne, where he directed the E3I2 laboratory (EA3876) from September 2001 to December 2011.
In January 2012, he joined the Lab-STICC UMR CNRS 6285, where he was, from 2012 to 2020, the head of the PIM (Propagation and Multi-scale Interaction) team and, since 2021, has been the head of the SyPH (Photonic Systems and Microwaves) department. His research interests include the following areas:
Multiscale and Multiphysics EM modeling, the propagation and interaction of EM waves, active or passive observation systems (Radar, GNSS, etc.), radar signal and image processing (clutter, detection, location, and tracking of radar targets), Microwave remote sensing (including the inversion problem).
He is the author or co-author of more than 380 scientific articles in international journals and conferences.
He has assumed responsibility for more than 56 scientific projects contracted in partnership with industry and state organizations.
He is a member of the Editorial Board of three international journals.
He is an Expert with several agencies and organizations in France and abroad.