Situation assessment: an end-to-end process for the detection of objects of interest

International audienceIn this article, semi-automatic approaches are developed for wide area situation assessment in near-real-time. The two-step method consists of two granularity levels. The first entity assessment uses a new multi-target tracking algorithm (hybridization of GM-CPHD filter and MHT with road constraints) on GMTI data. The situation is then assessed by detecting objects of interest such as convoys with other data types (SAR, video). These detections are based on Bayesian networks and their credibilistic counterpart

Track initiation using sparse radar data for low earth orbit objects

International audienceThis paper deals with the track-initiation problem of low Earth orbit objects observed by a space surveillance radar system of wide cross-elevation, narrow elevation sized field of view. This sensor configuration involves short arcs from which no orbital state can be computed, making regular tracking techniques not applicable. However, a set of short arcs may contain enough information to deduce such a state, the main problem being to associate them due to the high number of objects and false alarms. Recently, a method to limit the association possibilities of short arcs at one revolution of interval has been proposed. In this paper, an approach to estimate the state (six orbital elements) starting from two short arcs at one revolution of interval is presented, in order to enable track initiation in a multi-target tracking algorithm such as Track- Oriented Multi-Hypothesis Tracking. The method is based on the geometrical determination of four orbital elements, enabling the association of a third short arc to find the two remaining orbital elements. The following hypotheses on the ground radar are made to stick to current specifications of space surveillance systems being designed: south-oriented, monostatic, wide cross-elevation (160 ), narrow elevation (2 ) field of view, provides range, azimuth and elevation measurements. To simulate detections from the ground radar, we use real data from the Space-Track Two-Lines Elements, a space objects catalog provided by USSTRATCOM, combined with an SGP4 propagator. The principle of the presented approach follows three steps: First, the semi-major axis, the inclination, the right ascension of ascending node and the mean anomaly are retrieved from geometrical considerations. Then, the covariance matrix of the obtained state-vector is computed using a Monte-Carlo method, added to a suited process noise covariance matrix. The resulting distribution is propagated at the times of new observations using an unscented transform to assess their correlation. Finally, an iterative Newton-Gauss least square algorithm is used on the set of three correlated short arcs to find the values of the eccentricity and argument of perigee. The resulting state may be used in regular tracking techniques. The principle and functioning of the method on realistic simulation are presented, as well as its performance and limiting cases

Track Initiation of Low-Earth-Orbit Objects using Statistical Modeling of Sparse Observations

International audienceIn this paper, we investigate a new track initiation technique enabling the use of a low-cost radar system for Low-Earth-Orbit surveillance. This technique is based on a first association of observations with little ambiguity followed by a fast Initial Orbit Determination. This study supports the feasibility of the system as this technique shows a coverage of 84,4% within 6 days, with a combinatorial complexity kept under control when assessed in a realistic multitarget tracking context

Activation and Deactivation of a Robust Immobilized Cp*Ir-Transfer Hydrogenation Catalyst: A Multielement in Situ X-ray Absorption Spectroscopy Study

A highly robust immobilized [Cp*IrCl2]2 precatalyst on Wang resin for transfer hydrogenation, which can be recycled up to 30 times, was studied using a novel combination of X-ray absorption spectroscopy (XAS) at Ir L3-edge, Cl K-edge, and K K-edge. These culminate in in situ XAS experiments that link structural changes of the Ir complex with its catalytic activity and its deactivation. Mercury poisoning and “hot filtration” experiments ruled out leached Ir as the active catalyst. Spectroscopic evidence indicates the exchange of one chloride ligand with an alkoxide to generate the active precatalyst. The exchange of the second chloride ligand, however, leads to a potassium alkoxide–iridate species as the deactivated form of this immobilized catalyst. These findings could be widely applicable to the many homogeneous transfer hydrogenation catalysts with Cp*IrCl substructure

Fusion de données pour la surveillance du champ de bataille

ln the ground battlefield surveillance domain, ground target tracking is considered to evaluate the situation. The tracking is made with the data of a GMTI (Ground Moving Target Indicator) sensor which only detect the moving targets. However, the classical tracking techniques can not be used in order to obtain better awareness of the situation. But, with the development of precise geographic information systems, we can fuse the GMTI data with the contextual information. The work presented in this thesis deals with the fusion between the basic tracking algorithms and the map information. Thal is why the road network is seen like a constraint in order to improve the motion models of a target. An IMM (Interacting Multiple Model) with a variable structure is presented and tested on simulated data. This algorithm takes in input the positions of the MTI reports and the targets radial velocitis in addition, the objects of interest can leave the road network that is why the problem of the off road motion models and on road motion models ctivation is studied according to the kinematics of the targets. FinaIJy, an approach is proposed to include the negative information (terrain obscuration, road direction) in the target tracking process.Dans le domaine de la surveiIlance du champ de bataille, la poursuite de cibles terrestres est un point crucial pour évaluer le comportement des forces présentent sur le théâtre des opérations. Cette poursuite peut être menée à partir des capteurs aéroportés GMTI (Ground Moving Target Indicator) qui détectent tous les objets en mouvement. Toutefois, les techniques classiques de trajectographie ne permettent pas d'établir une situation fiable de la scène. Cependant, avec le développement et la fiabilité des systèmes d'information géographique, il devient possible de fusionner les données GMTI avec toute l'information contextuelJe pour améliorer le pistage. Le travail présenté dans cette thèse s'intéresse à l'intégration de l'information cartographique dans les techniques usueIJes de trajectographie. Le réseau routier est alors considéré comme une contrainte et un algorithme IMM à structure variable, pour s'adapter à la topologie du réseau, est présenté et testé sur données simulées. L'algorithme prend en entrée la position des plots MTI mais aussi la vitesse radiale des plots. Lorsque cette dernière est éloignée statistiquement de la vitesse radiale prédite, le système risque de ne pas associer le plot à la piste et de perdre cette dernière. Dans ce cas, un facteur d'oubli momentané est utilisé afin d'éviter la perte de la piste. De plus, la problématique des entrées et sorties de route pour le pi stage d'objets d'intérêts est traitée en activant ou désactivant les modèles dynamiques sous contraintes. Par ailleurs, nous proposons une approche pour considérer l'information négative (i.e. absence de détection) suivant la nature du terrain et améliorer la continuité du pi stag

Terrain obscuration managment for multiple ground target tracking

International audienceMultiple ground targets tracking with a GMTI (Ground Moving Target Indicator) sensor is considered a challenging problem in order to establish battlefield assessment. An IMM algorithm with a variable structure is adapted to the road network and used to track multiple manoeuvring ground targets. However, the case of undetected targets due to terrain elevation or Doppler obscuration was not taken into account in our tracking process. In this paper, we present our approach to track ground targets with the possibility for the target to be undetected. The perceivability probability is computed to update the estimated state and a "sentinel" concept is used to palliate the association ambiguities when several targets enter and exit the same terrain mask

Convoy detection processing by using the hybrid algorithm (GMCPHD/VS--IMMC--MHT) and dynamic bayesian networks

Bets student paper awardInternational audienceConvoys are military objects of interests in certain applications like battlefield surveillance, that is why it is important to detect and track them in the midst of civilian traffic as part of the situation assess- ment. Our purpose is a process in two steps. The first is an original tracking algorithm appropriate for Ground Moving Target Indicator (GMTI) data based on the hybridization of a labeled GMCPHD (Gaussian Mix- ture Cardinalized Probability Hypothesis Density) and the VS-IMMC-MHT (Variable Structure - Interacting Multiple Model with Constraints - Multiple Hypothesis Tracking): one is very efficient to estimate the num- ber of targets and the other for the state estimates. Then, by using algorithm outputs and other data like video or SAR if they are available, vehicle aggregates are detected and their characteristic are introduced into a Dynamic Bayesian Network which processes the prob- ability for an aggregate to be a convoy. Finally, the number of targets belonging to the convoy is evaluated. This process is tested on a complex simulated scenario, our tracking algorithm is compared to classical ones and used to compute the probability to have convoys

Hybrid algorithms for multitarget tracking using MHT and GM-CPHD

International audienceThe Gaussian mixture cardinalized probability hypothesis density (GM-CPHD) is a new original algorithm for multitarget tracking adapted to false alarms, nondetection and closely spaced objects. It models the target set as a random finite set (RFS) and estimates the target state as the first-order moment of a joint probability distribution. In the classical version no track assignment is implemented; this is a limit to scene understanding in a multitarget context. A technique for choosing the peak to track association is therefore proposed. With this implementation the main strength of the GM-CPHD is shown: it drastically improves the performances concerning the estimation of the number of targets and gives acceptable performances concerning the state of each individual target even if targets are close together, but it cannot rival an interacting multiple model estimator with multiple hypothesis tracking (IMM-MHT) in regards to velocity estimation, which is also the case with other multitarget tracking algorithms not equiped with IMM. However, MHT performance decreases due to poor estimation of the number of targets when targets are close together. It is worth noting that combining a probability hypothesis density (PHD) filter with a multiple-model approach should improve the velocity estimation but is unnecessary because we have developed a hybrid algorithm, combining the precision of the estimation of the number of targets given by the GM-CPHD, used in a labeled version, with the precision of the estimation of each individual state given by the MHT. These noteworthy performances can be observed for individual targets as well as for convoys. This hybrid algorithm is extended by using an IMM-MHT with road constraints

A new multi-target tracking algorithm using random finite sets. Application to the detection of vehicle convoys

Bien qu'étudié depuis les années 70, le pistage multicible terrestre constitue un problème difficile, particulièrement dans un contexte MTI (Moving Target Indicator) où l'environnement est très complexe (nombre de cibles important, fausses alarmes, probabilité de détection inférieur à 1, pouvoir de résolution du capteur, biais spatial et temporel, manoeuvrabilité des cibles,...). Cependant, une classe de filtres apparue récemment, le PHD (Probability Hypothesis Density), propose des solutions innovantes face à cette problématique. Dans cet article, nous proposons une version hybride d'un filtre PHD avec un filtre MHT (Multiple Hypothesis Tracker), afin de combiner les avantages du MHT sur la précision dans l'estimation de l'état des cibles avec la précision de l'estimation du nombre de cibles dans la scène obtenue avec le PHD. Cette hybridation est le point de départ pour un processus de détection de convoi, réalisé grâce à l'utilisation des réseaux bayésiens dynamiques