摘要翻译：
具有多目标跟踪和监视能力的相控阵系统的资源管理是实现其全部潜力的关键。本文的工作旨在通过建立一个与一个著名的随机控制问题机器替换问题的类比来改进现有的时间平衡调度方法的性能。利用所建议的策略，调度器可以适应操作场景，而不会严重牺牲时间平衡调度器的实用性。更具体地说，数值实验表明，用该策略指导的调度器可以成功地将非机动目标的不必要航迹更新与航迹恶化目标的更新进行交易，从而使跟踪性能得到全面提高。
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英文标题：
《Performance Improvement of Time-Balance Radar Schedulers Through
  Decision Policies (Extended Version)》
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作者：
\"Omer \c{C}ay{\i}r, \c{C}a\u{g}atay Candan
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最新提交年份：
2017
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  The resource management of a phase array system capable of multiple target tracking and surveillance is critical for the realization of its full potential. Present work aims to improve the performance of an existing method, time-balance scheduling, by establishing an analogy with a well-known stochastic control problem, the machine replacement problem. With the suggested policy, the scheduler can adapt to the operational scenario without a significant sacrifice from the practicality of the time-balance schedulers. More specifically, the numerical experiments indicate that the schedulers directed with the suggested policy can successfully trade the unnecessary track updates, say of non-maneuvering targets, with the updates of targets with deteriorating tracks, say of rapidly maneuvering targets, yielding an overall improvement in the tracking performance.
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PDF链接：
https://arxiv.org/pdf/1712.0059