Approches bayésiennes pour le pistage radar de cibles de surface potentiellement manoeuvrantes

Abstract : As part of the ground or maritime surveillance by using airborne radars, one of the mainobjectives is to detect and track a wide variety of targets over time. These treatments are generallybased on Bayesian filtering to estimate recursively the kinematic parameters (position,velocity and acceleration) of the targets. It is based on the state-space representation and moreparticularly on the prior modeling of the target evolutions (uniform motion, uniformly acceleratedmotion, movement rotational, etc.). If maneuvering targets are tracked, several motionmodels, each with a predefined dynamic, are typically combined in a multiple-model structure.Although these approaches are relevant, improvements can be made at several levels, includinghow to select and define a priori the models to be used.In this framework, several issues must be addressed.1 / When using a multiple-model structure, it is generally considered two to three models. Thischoice is made in the algorithm design stage according to the system knowledge and the userexpertise. However, it does not exist in our knowledge tools or/and rules to define the types ofmotions and their associated parameters.2 / It is preferable that the choice of the motion model(s) is consistent with the type of targetto be tracked.3 / When a type of motion model is used, its parameters are fixed a priori but these values ??arenot necessarily appropriate in all phases of the movement. One of the major challenges is theway to define the covariance matrix of the model noise and to model its evolution.The work presented in this thesis consists of algorithmic solutions to the previous problemsin order to improve the estimation of target trajectories.First, we establish a dissimilarity measure based on Jeffrey divergence between probability densitiesassociated with two different state models. It is applied to the comparison of motion models.It is then used to compare a set of several state models. This study is then harnessed to providea method for selecting a priori models constituting multiple-model algorithms.Then we present non-parametric Bayesian models (BNP) using the Dirichlet process to estimatemodel noise statistics. This model has the advantage of representing multimodal noises withoutspecifying a priori the number of modes and their features. Two cases are treated. In the firstone, the model noise precision matrix is estimated for a single motion model without issue ofany a priori on its structure. In the second one, we take advantage of the structural forms ofprecision matrices associated to motion models to estimate only a small number of hyperparameters.For both approaches, the joint estimation of the kinematic parameters of the target andthe precision matrix of the model noise is led by particle filtering. The contributions includecalculating the distribution optimal importance in each case.Finally, we take advantage of methods known as joint tracking and classification (JTC) forsimultaneously leading the classification of the target and the inference of its parameters. Inthis case, each target class is associated with a set of evolution models. In order to achievethe classification, we use the target position measurements and the target extent measurementscorresponding to the projection of the target length on the line of sight radar-target. Note that this approach is applied in a single target tracking context and a multiple-target environment.
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Submitted on : Thursday, November 17, 2016 - 1:26:08 PM
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Clément Magnant. Approches bayésiennes pour le pistage radar de cibles de surface potentiellement manoeuvrantes. Autre. Université de Bordeaux, 2016. Français. ⟨NNT : 2016BORD0136⟩. ⟨tel-01398519⟩



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