Publications

Detection and identification of emitters provide vital information for defensive strategies in electronic intelligence. Based on a received signal containing pulses from an unknown number of emitters, this article introduces an unsupervised methodology for deinterleaving RADAR signals based on a combination of clustering algorithms and optimal transport distances. The first step involves separating the pulses with a clustering algorithm under the constraint that the pulses of two different emitters cannot belong to the same cluster. Then, as the emitters exhibit complex behavior and can be represented by several clusters, we propose a hierarchical clustering algorithm based on an optimal transport distance to merge these clusters. A variant is also developed, capable of handling more complex signals. Finally, the proposed methodology is evaluated on simulated data provided through a realistic simulator. Results show that the proposed methods are capable of deinterleaving complex RADAR signals.

Cet article introduit une nouvelle méthode pour désentrelacer et classifier les émetteurs d'un signal RADAR utilisant des distances de transport optimal. Une stratégie en deux étapes est développée et appliquée pour désentrelacer un signal RADAR : tout d'abord, un algorithme de clustering est utilisé pour séparer les données. Ensuite, les résultats (classes) du clustering initial sont incorporées dans un clustering agglomératif hiérarchique combiné à des distances de transport optimal utiliser pour fusionner (regrouper) les classes. Enfin, le processus d'identification est appliqué sur ces résultats du désentrelacement; la méthodologie est basée sur l'élaboration d'une distance entre un groupe d'impulsions et une description des caractéristiques d'un émetteur RADAR à partir d'une base de données de référence. La méthodologie est construite et évaluée sur des données simulées étiquetées.

L'identification d'émetteurs RADAR représente un enjeu primordial dans le domaine de la guerre électronique d'autant plus que ces émetteurs présentent des caractéristiques de plus en plus complexes. Ce papier présente une méthodologie de désentrelacement et d'identification d'émetteurs RADAR basée sur l'utilisation de distances de transport optimal pour désentrelacer les impulsions d'un nombre inconnu d'émetteurs, puis identifier ces émetteurs grâce à une base de données. La méthodologie est testée sur des données obtenues grâce à un simulateur de données.

Identifying unknown RADAR emitters from re-ceived pulses is an important problem in electronic intelligence. It is a difficult problem, as agile RADAR emitters can have complex characteristics, and measurements are corrupted by various noises (non-Gaussian noise, missing pulses, etc.). In this paper, we introduce a new classification method based on optimal transport distances between collected RADAR pulses and a priori known emitter classes. Compared to previously proposed methods, this method does not require a training step, it can deal with a large number of classes, and it is easily interpretable. The method is tested on data obtained by a realistic RADAR scene simulator.

In this paper, a two-step methodology is developed to deinterlace RADAR signals. We mainly worked from a data simulator to obtain a large diversity of signals representing typical RADARs. First, a clustering algorithm is used to separate the pulses in the F-PW (Frequency-Pulse Width) plane, then a phase of cluster agglomeration is performed using the cluster output of the previous step as a features for an hierarchical agglomerative clustering combined with optimal transport distances. Results on labeled simulated data are given.

The invention relates to a computer-implemented method for identifying a radar transmitter from a set of corresponding received pulses, each pulse being associated with a respective time of arrival. The method includes determining an observed signature of the radar transmitter based on a distribution of the time gaps between consecutive times of arrival. For each transmitter class among a plurality of predetermined transmitter classes, each transmitter class being associated with at least one expected signature, the method includes calculating a proximity score between the observed signature and each expected signature associated with said transmitter class. Each expected signature is a function of an expected distribution of the time gaps between consecutive times of transmission for said transmitter class, and for a predetermined pulse loss rate. The method also includes assigning the radar transmitter to the transmitter class associated with the expected signature that provides the best proximity score.

A method for deinterleaving radar pulses, implemented by a computer. The method includes implementing a first clustering algorithm for assigning each pulse to a corresponding first class, based on the associated frequency, duration and time of arrival. The method also includes, for each first class, the method includes estimating a respective average frequency and average pulse duration, and implementing a second clustering algorithm to group the first classes into second classes based on the corresponding average frequency and average duration. For each second class, the method includes determining a distribution of the times of arrival of the associated pulses. The method also includes implementing a third clustering algorithm for grouping together the second classes into third classes, based on optimal transport distances between the corresponding determined distributions of the times of arrival.