自适应目标跟踪的通用学习波形选择策略
Universal Learning Waveform Selection Strategies for Adaptive Target Tracking
摘要：
在有源传感器目标跟踪中，在线选择最佳波形一直是一个令人感兴趣的问题。许多传统的解决方案利用估计理论解释，其中使用测量误差的波形特定的Cramér-Rao下限来为每个跟踪步骤选择最佳波形。然而，这种方法仅在高SNR状态下有效，并且需要关于目标运动和测量模型的相当限制性的假设集合。此外，由于计算的问题，许多传统的方法是有限的短期，或近视，优化，即使雷达场景表现出很强的时间相关性。最近，强化学习已经被提出用于波形选择，其中问题被框定为马尔可夫决策过程（MDP），从而允许长期规划。然而，强化学习的一个主要限制是，对于现实的目标和信道动态，底层马尔可夫过程的记忆长度通常是未知的，并且需要一个更通用的框架。本文提出了一种通用的连续波形选择方案，该方案在任何雷达场景中渐近地实现贝尔曼最优性，该雷达场景可以建模为有限但未知的整数$U$的$U^{\text{th}}$阶马尔可夫过程。我们的方法是基于通用信源编码，其中一个固定的源被解析成可变长度的短语，以建立一个上下文树，这是作为一个概率模型的场景的行为领域的成熟的工具。我们表明，基于上下文树加权（CTW）方法的多字母版本的算法可以用来最佳地解决广泛的一类波形敏捷跟踪问题，同时对环境的行为作出最小的假设。
英文摘要：
Online selection of optimal waveforms for target tracking with active sensors has long been a problem of interest. Many conventional solutions utilize an estimation-theoretic interpretation, in which a waveform-specific Cramér-Rao lower bound on measurement error is used to select the optimal waveform for each tracking step. However, this approach is only valid in the high SNR regime, and requires a rather restrictive set of assumptions regarding the target motion and measurement models. Further, due to computational concerns, many traditional approaches are limited to near-term, or myopic, optimization, even though radar scenes exhibit strong temporal correlation. More recently, reinforcement learning has been proposed for waveform selection, in which the problem is framed as a Markov decision process (MDP), allowing for long-term planning. However, a major limitation of reinforcement learning is that the memory length of the underlying Markov process is often unknown for realistic target and channel dynamics, and a more general framework is desirable. This work develops a universal sequential waveform selection scheme which asymptotically achieves Bellman optimality in any radar scene which can be modeled as a $U^{\text{th}}$ order Markov process for a finite, but unknown, integer $U$. Our approach is based on well-established tools from the field of universal source coding, where a stationary source is parsed into variable length phrases in order to build a context-tree, which is used as a probabalistic model for the scene's behavior. We show that an algorithm based on a multi-alphabet version of the Context-Tree Weighting (CTW) method can be used to optimally solve a broad class of waveform-agile tracking problems while making minimal assumptions about the environment's behavior.
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。
