论文标题
CHIRP信号的瞬时频率的概率估计
Probabilistic Estimation of Instantaneous Frequencies of Chirp Signals
论文作者
论文摘要
当无法访问这些功能的真实形式时，我们提出了一种估计CHIRP信号及其瞬时频率函数的连续时间概率方法。我们的模型通过表示为非线性随机微分方程的非线性级联高斯过程来表示这些功能。然后，用随机过滤器和smohorth估计功能的后验分布。我们为高斯过程模型计算一个（后）cramér-rao下限，并在均方含义中为估计误差提供了理论上的上限。实验表明，所提出的方法在合成数据上的表现优于许多最新方法。我们还证明该方法可用于两个现实世界数据集的开箱即用。
We present a continuous-time probabilistic approach for estimating the chirp signal and its instantaneous frequency function when the true forms of these functions are not accessible. Our model represents these functions by non-linearly cascaded Gaussian processes represented as non-linear stochastic differential equations. The posterior distribution of the functions is then estimated with stochastic filters and smoothers. We compute a (posterior) Cramér--Rao lower bound for the Gaussian process model, and derive a theoretical upper bound for the estimation error in the mean squared sense. The experiments show that the proposed method outperforms a number of state-of-the-art methods on a synthetic data. We also show that the method works out-of-the-box for two real-world datasets.
