分数低阶α稳定分布噪声下EP潜伏期变化韧性自适应检测

A robust EP latency change adaptive detection algorithm under α-stable noise conditions

依据分数低阶矩理论和信号噪声的特性,提出了一种自适应检测EP潜伏期变化的新方法.这种方法基于反正切函数的单调有界和奇对称特性,对误差信号en(k)进行非线性变换,抑制了EP信号中的分数低阶α稳定分布噪声,有效保留了信号成分,在高斯和分数低阶α稳定分布噪声条件下具有良好的韧性,且避免了动态估计信号噪声α参数的困难.利用这种方法动态检测EP潜伏期的变化,具有较高的估计精度和较快的收敛速度.

Traditional EP latency change detection algorithms ignore inadequately nonGaussian characteristics of noises in EP signals, resulting in the degeneration under such noise conditions. The robust algorithms proposed in recent years have improved the convergence under fractional lower order αstable noise conditions. However, some limitations are still existing. This paper proposes a new adaptive EP latency change detection algorithm (referred to as NLAT) based on the fractional lower order moment and the nonlinear transform of the error function in the adaptive system. The new algorithm suppresses the fractional lower order αstable noises effectively without the need to estimate dynamically the values of signals and noises. This algorithm is of a high accuracy and convergence rate under both Gaussian and fractional lower order αstable noise conditions.