针对低空区域的防控问题,提出分布式组网探测体制。采用基站式发射、分布式接收、基于到达时间(time of arrival,TOA)的定位、组网探测等手段,采用分层跟踪与航迹管理有效抑制低空探测场景下的虚假航迹,适宜低空目标探测场景。通过引入...针对低空区域的防控问题,提出分布式组网探测体制。采用基站式发射、分布式接收、基于到达时间(time of arrival,TOA)的定位、组网探测等手段,采用分层跟踪与航迹管理有效抑制低空探测场景下的虚假航迹,适宜低空目标探测场景。通过引入分布式扩展系数和低空覆盖系数,可对系统进行有效评估。在正方形与正六边形部署场景下,仿真验证了其威力与精度性能,与传统单基地雷达相比,该体制具有较好的低空探测与扩展能力。展开更多
There is a great thrust in industry toward the development of more feasible and viable tools for storing fast-growing volume, velocity, and diversity of data, termed ＇big data＇. The structural shift of the storage m...There is a great thrust in industry toward the development of more feasible and viable tools for storing fast-growing volume, velocity, and diversity of data, termed ＇big data＇. The structural shift of the storage mechanism from traditional data management systems to NoSQL technology is due to the intention of fulfilling big data storage requirements. However, the available big data storage technologies are inefficient to provide consistent, scalable, and available solutions for continuously growing heterogeneous data. Storage is the preliminary process of big data analytics for real-world applications such as scientific experiments, healthcare, social networks, and e-business. So far, Amazon, Google, and Apache are some of the industry standards in providing big data storage solutions, yet the literature does not report an in-depth survey of storage technologies available for big data, investigating the performance and magnitude gains of these technologies. The primary objective of this paper is to conduct a comprehensive investigation of state-of-the-art storage technologies available for big data. A well-defined taxonomy of big data storage technologies is presented to assist data analysts and researchers in understanding and selecting a storage mecha- nism that better fits their needs. To evaluate the performance of different storage architectures, we compare and analyze the ex- isling approaches using Brewer＇s CAP theorem. The significance and applications of storage technologies and support to other categories are discussed. Several future research challenges are highlighted with the intention to expedite the deployment of a reliable and scalable storage system.展开更多
文摘针对低空区域的防控问题,提出分布式组网探测体制。采用基站式发射、分布式接收、基于到达时间(time of arrival,TOA)的定位、组网探测等手段,采用分层跟踪与航迹管理有效抑制低空探测场景下的虚假航迹,适宜低空目标探测场景。通过引入分布式扩展系数和低空覆盖系数,可对系统进行有效评估。在正方形与正六边形部署场景下,仿真验证了其威力与精度性能,与传统单基地雷达相比,该体制具有较好的低空探测与扩展能力。
文摘There is a great thrust in industry toward the development of more feasible and viable tools for storing fast-growing volume, velocity, and diversity of data, termed ＇big data＇. The structural shift of the storage mechanism from traditional data management systems to NoSQL technology is due to the intention of fulfilling big data storage requirements. However, the available big data storage technologies are inefficient to provide consistent, scalable, and available solutions for continuously growing heterogeneous data. Storage is the preliminary process of big data analytics for real-world applications such as scientific experiments, healthcare, social networks, and e-business. So far, Amazon, Google, and Apache are some of the industry standards in providing big data storage solutions, yet the literature does not report an in-depth survey of storage technologies available for big data, investigating the performance and magnitude gains of these technologies. The primary objective of this paper is to conduct a comprehensive investigation of state-of-the-art storage technologies available for big data. A well-defined taxonomy of big data storage technologies is presented to assist data analysts and researchers in understanding and selecting a storage mecha- nism that better fits their needs. To evaluate the performance of different storage architectures, we compare and analyze the ex- isling approaches using Brewer＇s CAP theorem. The significance and applications of storage technologies and support to other categories are discussed. Several future research challenges are highlighted with the intention to expedite the deployment of a reliable and scalable storage system.
