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Fully-Adaptive Update Rate for Nonlinear Trackers

Abstract : Modern phased-array multifunction radars have the ability to change or schedule the tasks of the beam in order to accomplish all their missions in an optimized fashion. The resource manager of the radar must then control the update rate of the measurement task of target active tracking, so as to minimize the radar computer load without losing targets. Measurements scarce in time lead to low radar load, but then at each measurement time an increased number of illuminations may be required to find the target, and thus increase the load in turn. Based upon this rationale, a sound procedure was proposed by Blackman and Van Keuk in [1] to derive an optimal measurement rate. Their optimization criterion is established using a linear Singer target model and a linear Kalman filter. In this paper their method is extended, and we propose a versatile optimal update rate algorithm that is applicable to virtually any nonlinear target model combined with any nonlinear filter able to output an error covariance matrix. This includes EKF, UKF, IMM, and particle filters. For numerical experiments and validation we consider a nonlinear target model based on Frenet-Serret 3D equations, and the tracking is performed by a nonlinear Invariant Extended Kalman Filter (IEKF).
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Contributeur : Silvere Bonnabel Connectez-vous pour contacter le contributeur
Soumis le : jeudi 24 janvier 2019 - 14:35:51
Dernière modification le : samedi 22 octobre 2022 - 05:16:08


  • HAL Id : hal-01982794, version 1


M Pilté, Silvere Bonnabel, F Barbaresco. Fully-Adaptive Update Rate for Nonlinear Trackers. IET Radar Sonar and Navigation, 2018. ⟨hal-01982794⟩



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