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- W2885704885 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 optimised fashion. The resource manager of the radar must then control the update rate of the measurement task of target active tracking, so as to minimise the radar computer load without losing targets. Scarce measurements lead to low radar load, but they also lead to an increased number of illuminations at each measurement epoch to find the target. On the basis of this rationale, a sound procedure was proposed by Blackman and van Keuk to derive an optimal measurement rate. Their optimisation criterion is established using a linear Singer target model and a linear Kalman filter. In this study, their method is extended, and the authors propose a versatile optimal update rate algorithm that is applicable to virtually any non-linear target model combined with any non-linear filter able to output an error covariance matrix. This includes extended Kalman filter (EKF), Unscented Kalman Filter (UKF), Interacting Multiple Model (IMM) algorithm, and particle filters. For numerical experiments and validation, they consider a non-linear target model based on Frenet–Serret three-dimensional equations, and the tracking is performed by a non-linear invariant EKF." @default.
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- W2885704885 date "2018-12-01" @default.
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- W2885704885 title "Fully adaptive update rate for non‐linear trackers" @default.
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- W2885704885 doi "https://doi.org/10.1049/iet-rsn.2018.5252" @default.
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