Dynamic management of tracking ressources for hyper-manoeuvring targets

Abstract : The new generation of radars is facing increasingly threatening targets. These radars are asked to perform several tasks in parallel, including surveillance and tracking. To this aim, they can be equipped with staring antennas, so they overcome the constraints induced by the rotation of the antenna. The tracking function of the radar has thus to be upgraded to respond to the double issue of tracking highly manoeuvring targets and managing the resources to balance time between tasks. In this context, this thesis investigates new means of tracking highly manoeuvring targets. A new target model based on intrinsic coordinates to perform target tracking is proposed. This new target model is expressed in the frame of the target itself, and uses the Frenet-Serret frame, which is well suited to the description of highly dynamic manoeuvres involving normal accelerations that are much larger than earth gravity. A filtering algorithm using the special intrinsic formulation of the target model is developed. This filtering algorithm is very similar in terms of implementation to an Extended Kalman filter, and was implemented using real data. The comparison with standard target models and filtering algorithms show improvements over simple models and algorithms on a large set of trajectories. A new estimation method, relying on the least squares formulation of the smoothing approach, and taking into account kinematic jumps in the trajectory is also developed. This method also shows improvements over a set of common algorithms based on standard manoeuvre detection. And independently, we investigate the issue of update rate adaptation for radar measurements. A very general update rate adaptation algorithm is derived to optimise the time of revisit of each target, allowing to preserve the radar time budget for other tasks simultaneously performed, such as surveillance.
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Marion Pilté. Dynamic management of tracking ressources for hyper-manoeuvring targets. Automatic. PSL Research University, 2018. English. ⟨NNT : 2018PSLEM068⟩. ⟨tel-02274370⟩

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