%0 Journal Article
%T Multiple Target Tracking Using Cheap Joint Probabilistic Data Association Multiple Model Particle Filter in Sensors Array
%A Messaoudi Zahir
%A Oussalah Mourad
%A Ouldali Abdelaziz
%J International Journal of Artificial Intelligence & Applications
%D 2012
%I Academy & Industry Research Collaboration Center (AIRCC)
%X Joint multiple target tracking and classification is an important issue in many engineering applications. Inrecent years, multiple sensor data fusion has been extensively investigated by researchers in a variety ofdisciplines. Indeed, combining results issued from multiple sensors can provide more accurate informationthan using a single sensor. In the present paper we address the problem of data fusion for joint multiplemaneuvering target tracking and classification in cluttered environment where centralized versusdecentralized architectures are often opposed. The proposal advocates a hybrid approach combining aParticle Filter (PF) like method to deal with system nonlinearities and Fitgerald¡¯s Cheap JointProbabilistic Data Association Filter CJPDAF for the purpose of data association and target estimationproblems, yielding CJPDA-PF algorithm. While the target maneuverability is tackled using a combinationof a Multiple Model Filter (MMF) and CJPDAF, which yields CJPDA-MMPF algorithm. Consequently, ateach particle level (of the particle filter), the state of the particle is evaluated using the suggested CJPDAMMF. In case of several sensors, the centralized fusion architecture and the distributed architecture in thesense of Federated Kalman Filtring are investigated and compared. The feasibility and the performances ofthe proposal have been demonstrated using a set of Monte Carlo simulations dealing with two maneuveringtargets with two distinct operation modes and various clutter densities.
%K Data fusion
%K Multiple manoeuvring targets tracking
%K JPDA
%K MMPF
%U http://airccse.org/journal/ijaia/papers/3412ijaia01.pdf