用于敏捷雷达脉冲序列流分析的贝叶斯非参数隐马尔可夫模型
Bayesian Non-parametric Hidden Markov Model for Agile Radar Pulse Sequences Streaming Analysis
AI总结：
针对多功能雷达工作模式识别难的问题，提出带agile BNP-HMM模型和流式变点检测的在线处理框架，性能接近误差下界，在模拟数据集上比基准方法更高效鲁棒。
摘要：
多功能雷达（MFRs）是一类复杂的传感器，具备实现复杂捷变脉间调制和动态工作模式调度的能力。MFR的发展给现代电子侦察系统或雷达告警接收机的MFR工作模式识别与推理带来了巨大挑战。为解决这一问题，本文提出一种用于MFR工作模式参数估计和变点检测的在线处理框架。首先，本文设计了一种带有自定义先验分布的完全共轭贝叶斯非参数隐马尔可夫模型（agile BNP-HMM），用以表征MFR脉冲捷变特性，该模型支持完全变分贝叶斯推理。随后，所提框架由两大部分构成：第一部分是agile BNP-HMM模型，用于自动推理HMM隐状态的数量以及对应隐状态的发射分布，本文推导了性能的估计误差下界，并证明所提算法的性能接近该下界；第二部分利用流式贝叶斯更新提升计算效率，并基于加权序贯概率比检验设计了在线工作模式变化检测框架。我们在多种模拟数据集上验证表明，相较于基准方法，所提框架始终具备高效性与鲁棒性。
英文摘要：
Multi-function radars (MFRs) are sophisticated types of sensors with the capabilities of complex agile inter-pulse modulation implementation and dynamic work mode scheduling. The developments in MFRs pose great challenges to modern electronic reconnaissance systems or radar warning receivers for recognition and inference of MFR work modes. To address this issue, this paper proposes an online processing framework for parameter estimation and change point detection of MFR work modes. At first, this paper designed a fully-conjugate Bayesian non-parametric hidden Markov model with a designed prior distribution (agile BNP-HMM) to represent the MFR pulse agility characteristics. The proposed model allows fully-variational Bayesian inference. Then, the proposed framework is constructed by two main parts. The first part is the agile BNP-HMM model for automatically inferring the number of HMM hidden states and emission distribution of the corresponding hidden states. An estimation error lower bound on performance is derived and the proposed algorithm is shown to be close to the bound. The second part utilizes the streaming Bayesian updating to facilitate computation, and designed an online work mode change detection framework based upon a weighted sequential probability ratio test. We demonstrate that the proposed framework is consistently highly effective and robust to baseline methods on diverse simulated data-sets.
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。
