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.. La-littérature-et-limites-actuelles, 226 A.1.2 Contributions, p.229

.. Le-problème-du-suivi-comportementalcible, 235 A.3.2 État de l'art et limites actuelles, 236 A.3.3 Les simulateurs avancés de comportements à base d'agents autonomes: une alternative aux DBNs . . . . . . . . . . . . . . . . . . . . . . . 237

. Arulampalam, approche proposée a été implémentée en utilisant SE-Star, un simulateur comportemental propriétaire de la société Thales 21 . Cependant, SE-Star est un simulateur avec peu d'aléatoire dans la dynamique du modèle de comportement. Par conséquent, son utilisation dans un ltre particulaire peut conduire Pour cette raison, nous avons mis en ÷uvre un ltre particulaire régularisé (voir Section A.2.4), A.3.5 Implémentation et Expérimentations L, 2002.

. Comme-dans-le-cas-mono-cible, le suivi multi-cibles est un problème de ltrage et par conséquent, il peut être modélisé à l'aide des système dynamique partiellement observés. Dans ce cas l'état globale du système x t est composé de l'ensemble des états de tous les cibles suivies. De même, l'observation z t obtenue des capteurs est composée d'un ensemble d'observations atomiques correspondant chacune à une cible observée. Les approches de ltrage classiques peuvent aisément être appliquées au problème du suivi multi-cibles. Toutefois, parce qu'elles raisonnent dans un espace d'état de très grande dimension, ces approches sont généralement inapplicable lorsque le nombre de cibles est supérieure à 3

. Toutefois and . De-manière-générale, le suivi multi-cibles présente deux dicultés supplémentaires en comparaison au suivi mono-cible: 21 Thales est une société française multinationale spécialisée dans l'aérospatial

A. Figure, 8: Suivi multi-cibles comme une collection de suivis mono-cible

ˆ. Les, dépendances comportementales) entre cibles: la dynamique d'une cible donné peut être inuencée par la présence d'autres cibles dans son voisinage, Il est donc primordiale de prendre en compte ces dépendances lors du processus de ltrage

. Le-problème-d, association de données observationnelles a été étudié pendant des décennies et des solutions classiques ont été proposées telles que le traqueur à multiples hypothèses (MHT) [Reid, 1979] et, 1983.

D. Khan, Cattelani 2014] qui ne présupposent pas d'une indépendance totale des cibles et traitent explicitement les interactions entre elles. Cependant, soit ces travaux]) s'appuient sur des hypothèses spéciques au problème adressé ce qui leur permet d'avoir une solution d'une complexité faible, soit ces travaux ([Cattelani et al., 2014]) proposent des méthodologies génériques intégrant des modèles d'interactions pouvant s'appliquer à d'autres problèmes. Toutefois, dans ce dernier cas, 2003.

. Dans-ce-mémoire, avance, ne change pas au cours du temps Nous focalisons notre attention sur la gestion des interaction entre cibles et nous nous appuyons sur l'approche JPDA pour résoudre les problèmes d'association de données observationnelles. Plus précisément, nous sommes motivés par l'idée de développer une approche générique (c-à-d, à base de modèles d'interactions) permettant de gérer, à faible coûts computationnels, les interactions entre cibles dans un processus d'inférence comportementale. Dans les sections suivantes, nous allons formaliser le problème du suivi-multi (Section A.4.1), introduire l'approche JPDA pour la résolution du problème d