论文标题
通过广义LMPU测试对小频率变化的低复杂性检测
Low-Complexity Detection of Small Frequency Changes by the Generalized LMPU Test
论文作者
论文摘要
在本文中，我们考虑检测正弦信号频率的微小变化，这是在各种信号处理应用中产生的。此问题的广义似然比测试（GLRT）使用频率的最大似然（ML）估计器，因此具有较高的计算复杂性。此外，GLRT不一定是最佳的，其性能可能会降解，而非肌电场景的特征是密切的假设和小样本量。在本文中，我们提出了一种新的检测方法，称为局部最强大的无偏（GLMPU）测试，这是在存在滋扰参数的情况下局部检测的一种通用方法。在测量信号的复杂幅度未知的情况下，开发了GLMPU检验的封闭形式的表达，以检测频率偏差。数值模拟在检测性能和计算复杂性的概率方面显示出对GLRT的性能的提高。
In this paper, we consider the detection of a small change in the frequency of sinusoidal signals, which arises in various signal processing applications. The generalized likelihood ratio test (GLRT) for this problem uses the maximum likelihood (ML) estimator of the frequency, and therefore suffers from high computational complexity. In addition, the GLRT is not necessarily optimal and its performance may degrade for non-asymptotic scenarios that are characterized by close hypotheses and small sample sizes. In this paper we propose a new detection method, named the generalized locally most powerful unbiased (GLMPU) test, which is a general method for local detection in the presence of nuisance parameters. A closed-form expression of the GLMPU test is developed for the detection of frequency deviation in the case where the complex amplitudes of the measured signals are unknown. Numerical simulations show improved performance over the GLRT in terms of probability of detection performance and computational complexity.
