Frédéric Brigui

Analysis and comparison of MIMO radar waveforms

Choosing a proper waveform is a critical task for the implementation of multiple-input multiple-output (MIMO) radars. In addition to the general requirements for radar waveforms such as good resolution, low sidelobes, etc, MIMO radar waveforms also should possess good orthogonality. In this paper we give a brief overview of MIMO radar waveforms, which are classified into four categories: (1) time division multiple access (TDMA), (2) frequency division multiple access (FDMA), (3) Doppler division multiple access (DDMA), and (4) code division multiple access (CDMA). A special circulating MIMO waveform is also addressed The properties as well as application limitations of different waveforms are analyzed and compared. Some simulations results are also presented to illustrate the respective performance of different waveforms.

New SAR target imaging algorithm based on oblique projection for clutter reduction

We have developed a new synthetic aperture radar (SAR) algorithm based on physical models for the detection of a man-made target (MMT) embedded in strong clutter (trunks in a forest). The physical models for the MMT and the clutter are represented by low-rank subspaces and are based on scattering and polarimetric properties. Our SAR algorithm applies the oblique projection of the received signal along the clutter subspace onto the target subspace. We compute its statistical performance in terms of probabilities of detection and false alarms. The performances of the proposed SAR algorithm are improved compared to those obtained with existing SAR algorithms: the MMT detection is greatly improved, and the clutter is rejected. We also studied the robustness of our SAR algorithm to interference modeling errors. Results on real foliage penetration data showed the usefulness of this approach.

New SAR Algorithm Based on Orthogonal Projections for MMT Detection and Interference Reduction

We develop a new synthetic aperture radar (SAR) algorithm based on physical models for the detection of a man-made target (MMT) embedded in strong interferences (trunks of a forest). These physical models for the MMT and the interferences are integrated in low-rank subspaces and are based on scattering and polarimetric properties. Several images, called subspace SAR images, can be generated and combined considering these subspace models. We then propose a new approach for target detection and interference reduction based on the combination of SAR subspace images. We show that our SAR algorithm outperforms the classical SAR imagery algorithm on both simulated data and real data in the context of foliage penetration detection.

Multidimensional low-rank filter based on the AU-HOSVD for MIMO STAP

We propose in this paper a new low rank filter for MIMO STAP (Multiple Input Multiple Output Space Time Adaptive Processing) based on the AU-HOSVD (Alternative Unfolding Higher Order Singular Value Decomposition). This decomposition called the AU-HOSVD is able to process data in correlated dimensions which is desirable for STAP methods. We apply the new filter to MIMO STAP simulated data. The results are encouraging and outperforms the conventional STAP 2D filter in terms of number of secondary data.

New Low-Rank Filters for MIMO-STAP Based on an Orthogonal Tensorial Decomposition

We develop in this paper a new adaptive low-rank (LR) filter for MIMO-space time adaptive processing (STAP) application based on a tensorial modeling of the data. This filter is based on an extension of the higher order singular value decomposition (HOSVD) (which is also one possible extension of singular value decomposition to the tensor case), called alternative unfolding HOSVD (AU-HOSVD), which allows us to consider the combinations of dimensions. This property is necessary to keep the advantages of the STAP and the MIMO characteristics of the data. We show that the choice of a good partition (as well as the tensorial modeling) is not heuristic but have to follow several features. Thanks to the derivation of the theoretical formulation of multimode ranks for all partitions, the tensorial LR filters are easy to compute. Results on simulated data show the good performance of the AU-HOSVD LR filters in terms of secondary data and clutter notch.

Analyse et comparaison de formes d’ondes pour le radar MIMO

REE N°5/2015 47 RADARS A ANTENNES ELECTRONIQUES RADAR 2014 Analyse et comparaison de formes d’ondes pour le radar MIMO Par Hongbo Sun1 , Frederic Brigui2 , Marc Lesturgie2 Temasek Laboratories@NTU - Nanyang Technological University1 , Departement electromagnetique et radar ONERA - The French Aerospace Lab2 Choosing a proper waveform is a critical task for the implementation of multiple-input multiple-output (MIMO) radars. In addition to the general requirements for radar waveforms such as good resolution, low sidelobes, etc. MIMO radar waveforms also should possess good orthogonality. In this paper we give a brief over- view of MIMO radar waveforms, which are classified into four categories: (1) time division multiple access (TDMA), (2) frequency division multiple access (FDMA), (3) Doppler division multiple access (DDMA), and (4) code division multiple access (CDMA). A special circulating MIMO waveform is also addressed. The properties, as well as applica- tion limitations of different waveforms, are analyzed and compared. Some simulations results are also presented to illustrate the respective performance of different waveforms. ABSTRACT Introduction De par son archite