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- W4226160107 abstract "In tracking radar, the sensing environment often varies significantly over a track duration due to the target's trajectory and dynamic interference. Adapting the radar's waveform using partial information about the state of the scene has been shown to provide performance benefits in many practical scenarios. Moreover, radar measurements generally exhibit strong temporal correlation, allowing memory-based learning algorithms to effectively learn waveform selection strategies. This work examines a radar system which builds a compressed model of the radar-environment interface in the form of a context-tree. The radar uses this context tree-based model to select waveforms in a signal-dependent target channel, which may respond adversarially to the radar's strategy. This approach is guaranteed to asymptotically converge to the average-cost optimal policy for any stationary target channel that can be represented as a Markov process of order <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$U < infty$</tex> , where the constant <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$U$</tex> is unknown to the radar. The proposed approach is tested in a simulation study, and is shown to provide tracking performance improvements over two state-of-the-art waveform selection schemes." @default.
- W4226160107 created "2022-05-05" @default.
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- W4226160107 date "2021-11-29" @default.
- W4226160107 modified "2023-10-03" @default.
- W4226160107 title "Waveform Selection for Radar Tracking in Target Channels With Memory via Universal Learning" @default.
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- W4226160107 doi "https://doi.org/10.1109/milcom52596.2021.9653086" @default.
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