New results on FIR system identification using higher-order statistics
New results on FIR system identification using higher-order statistics
复制标题
使用高阶统计量识别 FIR 系统的新结果
DOI：
10.1109/78.91178
复制
发表时间：
1990
期刊：
影响因子：
--
通讯作者：
Jitendra Tugnait
中科院分区：
文献类型：
--
作者：
Jitendra Tugnait
考虑了由系统输出噪声观测的累积量统计量估计滑动平均模型参数的问题。该系统由i.i.d.驱动。（独立同分布）非高斯序列，不可观测。噪声是加性的，并且可以是有色的和非高斯的。重新参数化现有的线性方法，并修改它，进行了讨论。仿真结果表明，在无噪声的情况下，在数值条件下，reparametrized算法及其修改，一个显着的改善。对于i.i.d.的情况。噪声，重新参数化的算法表现出显着的性能下降，而其修改降级更优雅。<<ETX>>
The problem of estimating the parameters of a moving average model from the cumulant statistics of the noisy observations of the system output is considered. The system is driven by an i.i.d. (independent and identically distributed) non-Gaussian sequence that is not observed. The noise is additive and may be colored and non-Gaussian. Re-parametrization of an existing linear method, and a modification to it, are discussed. Simulation results show a distinct improvement in the numerical conditioning of both, the reparametrized algorithm and its modification, for the noisefree case. For the case of i.i.d. noise, the reparametrized algorithm shows a marked degradation in performance whereas its modification degrades far more gracefully.<<ETX>>