【摘 要】
：
非高斯非平稳随机信号处理是当前信号处理领域的研究热点,具有重要的理论意义和实际价值.采用TVAR模型来描述非平稳随机信号,在α稳定分布噪声条件下,传统的递推最小二乘(RLS
【机 构】
：
大连理工大学电子信息与电气工程学部,大连工业大学信息科学与工程学院,
论文部分内容阅读
非高斯非平稳随机信号处理是当前信号处理领域的研究热点,具有重要的理论意义和实际价值.采用TVAR模型来描述非平稳随机信号,在α稳定分布噪声条件下,传统的递推最小二乘(RLS)算法效果显著退化.采用最小p范数(LPN)算法对TVAR模型的时变参数进行估计,仿真实验结果表明,LPN算法不仅适用于高斯条件而且适用于非高斯α稳定分布噪声条件,且与仅适用于高斯条件下的RLS算法相比具有更好的韧性.
Non-Gaussian nonstationary random signal processing is the hotspot in the field of signal processing and has important theoretical and practical value.Using TVAR model to describe nonstationary random signals, under the condition of αstable distribution noise, the traditional recursive least squares (RLS) algorithm is significantly degraded.The LPV algorithm is used to estimate the time-varying parameters of TVAR model.The simulation results show that the LPN algorithm is not only suitable for Gaussian conditions but also suitable for non-Gaussian α-stable distributed noise conditions, And has better toughness than the RLS algorithm which is only suitable for Gaussian condition.
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