协作多源信号检测的贝叶斯框架
A Bayesian Framework for Collaborative Multi-Source Signal Detection
摘要：
本文介绍了一种贝叶斯框架来检测多个信号嵌入在噪声观测传感器阵列。对于各种状态的知识的通信信道和噪声在接收传感器，边缘化过程的基础上最近的工具的有限随机矩阵理论，结合最大熵原理，用于计算假设选择标准。非常值得注意的是，明确的表达式推导出贝叶斯检测器，使决定在嘈杂的无线环境中的信号源的存在。所提出的贝叶斯检测器优于经典的功率检测器时，噪声功率是已知的，并提供了非常好的性能有限的知识的噪声功率。仿真证实了理论结果和量化的增益实现使用建议的贝叶斯框架。
英文摘要：
This paper introduces a Bayesian framework to detect multiple signals embedded in noisy observations from a sensor array. For various states of knowledge on the communication channel and the noise at the receiving sensors, a marginalization procedure based on recent tools of finite random matrix theory, in conjunction with the maximum entropy principle, is used to compute the hypothesis selection criterion. Quite remarkably, explicit expressions for the Bayesian detector are derived which enable to decide on the presence of signal sources in a noisy wireless environment. The proposed Bayesian detector is shown to outperform the classical power detector when the noise power is known and provides very good performance for limited knowledge on the noise power. Simulations corroborate the theoretical results and quantify the gain achieved using the proposed Bayesian framework.
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