Rémi Durand

New SAR Processor Based on Matched Subspace Detectors

This paper deals with a new synthetic aperture radar (SAR) processor based on a subspace detector designed for man-made target (MMT) detection. As MMTs are more accurately described by a set of canonical elements than with isotropic points, we develop a new algorithm which aims at using new models, instead of the isotropic point model commonly used in SAR processors. A subspace detector matched to canonical elements is included in the SAR processing. The implementation and the optimization of subspace detector SAR (SDSAR) algorithm is described. Simple examples of MMT detection in simulations and real data with a target hidden in a forest show the power of our approach. The SDSAR algorithm is shown to be the first robust and tractable algorithm relying on realistic scattering assumptions about the target.

SAR Processors Based on Signal or Interference Subspace Detector

In this paper, we present a new synthetic aperture radar (SAR) processor relying on subspace detectors. The scattering pattern of the constituent element of the man-made target (MMT) to detect is used to improve the detection capabilities. In the particular case of foliage penetration (FOPEN), we propose to reduce the probability of false alarms by considering the scattering pattern of almost vertical trunks (modeled here as dielectric cylinders). New subspace detectors are derived to look for either an interference or a target element in a noisy environment. A new processor based on these subspace detectors is successively applied to simulated data. In addition, results obtained with real data reveal a significant improvement in the detection capabilities both in comparison with a classical SAR processor and with the subspace signal detector SAR (SSDSAR) processor (without interference model).

Back Projection Version of Subspace Detector SAR Processors

A new version of the signal subspace detector synthetic aperture radar (SSDSAR) processor developed in [1] is proposed. Actually, the SSDSAR processor can be divided in two different parts: a subspace basis computation part and an imaging part. The new version proposed here consists in performing the imaging part on fast-time compressed data and not raw data in order to reduce the computational cost from O(N4) to O(N3). We note that this new processor is the subspace detector version of the classical back projection SAR processor.

SAR Processor based on a CFAR Signal or Interference Subspace Detector Matched to Man Made Target Detection in a Forest

This paper deals with two new SAR processors based on CFAR subspaces detector. These two algorithms aim at improving man made target detection performances in a forest clutter by using electromagnetic scattering models. The implementation of the two detectors is described. An application on simulated data shows the interest of the two methods.

Man Made Target Detection in a Forest with a Subspace Detector SAR Processor

This paper deals with the capability of a SAR processor based on a subspace detector to get better performance than a classical SAR processor for Man Made Target (MMT) detection in a forest. The new algorithm aims at using new models, different from the isotropic point one commonly used in SAR processors. The implementation of the Subspace Detector SAR (SDSAR) algorithm is described and detection performances between a classical SAR (CSAR) algorithm and the SDSAR one are compared when detecting a MMT in white Gaussian noise and in a simulated forest.