摘要翻译：
众所周知，雷达对目标的分类非常困难，最好的系统还没有达到足够高的性能和可靠性水平。在当前的贡献中，我们探索了一种新的基于雷达的目标识别设计，其中角分集用于认知方式以获得更好的性能。与传统分类方案相比，性能是基准的。该方案易于推广到基于多分集策略的认知目标识别中。
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英文标题：
《A cognitive diversity framework for radar target classification》
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作者：
Amit K. Mishra and Chris Baker
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Classification of targets by radar has proved to be notoriously difficult with the best systems still yet to attain sufficiently high levels of performance and reliability. In the current contribution we explore a new design of radar based target recognition, where angular diversity is used in a cognitive manner to attain better performance. Performance is bench- marked against conventional classification schemes. The proposed scheme can easily be extended to cognitive target recognition based on multiple diversity strategies.
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PDF链接：
https://arxiv.org/pdf/1110.6589