arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2108.01181cs.ITcs.LGeess.SPmath.IT
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基于泛学习的有记忆目标通道跟踪波形选择

Waveform Selection for Radar Tracking in Target Channels With Memory via Universal Learning

Virginia Tech(弗吉尼亚理工大学);U.S Army Research Laboratory(美国陆军研究实验室)

Charles E. Thornton, R. Michael Buehrer, Anthony F. Martone

摘要:

在跟踪雷达中,由于目标的运动轨迹和动态干扰的影响,传感环境在跟踪过程中经常发生显著变化。使用关于场景状态的部分信息来调整雷达的波形已经被示出在许多实际场景中提供性能益处。此外,雷达测量通常表现出很强的时间相关性,允许基于存储器的学习算法有效地学习波形选择策略。本文研究了一种雷达系统,它以上下文树的形式建立了雷达-环境界面的压缩模型。雷达使用这种基于上下文树的模型来选择信号相关目标通道中的波形,这可能会对雷达的策略做出不利的响应。该方法保证渐近收敛到平均成本最优策略的任何固定的目标信道,可以表示为一个马尔可夫过程的顺序U < $\infty$,其中常数U是未知的雷达。所提出的方法进行了测试,在仿真研究中,并提供跟踪性能的改善,超过两个国家的最先进的波形选择方案。

英文摘要:

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 U < $\infty$, where the constant U 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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