论文标题
贝叶斯对正弦信号的检测频率随机变化
Bayesian Detection of a Sinusoidal Signal with Randomly Varying Frequency
论文作者
论文摘要
检测具有随机变化频率的正弦信号的问题具有较长的历史。它是信号处理中的核心问题之一，在许多应用中引起，包括例如水下声学线跟踪，FM无线电通信的解调，光学通信中的激光相位漂移以及最近连续的引力波天文学。在本文中，我们描述了马尔可夫链基于蒙特卡洛的程序，以计算特定的检测后密度。我们通过模拟证明，我们的方法比基于隐藏的马尔可夫模型的解决方案高达25美元的检测率，这些解决方案通常被认为是这些问题的领先技术。
The problem of detecting a sinusoidal signal with randomly varying frequency has a long history. It is one of the core problems in signal processing, arising in many applications including, for example, underwater acoustic frequency line tracking, demodulation of FM radio communications, laser phase drift in optical communications and, recently, continuous gravitational wave astronomy. In this paper we describe a Markov Chain Monte Carlo based procedure to compute a specific detection posterior density. We demonstrate via simulation that our approach results in an up to $25$ percent higher detection rate than Hidden Markov Model based solutions, which are generally considered to be the leading techniques for these problems.
