SENIOR MEMBER, IEEE, Department of Electrical and Computer Engineering, Arizona State University, Tempe, AZ 85287.
IEEE Trans Pattern Anal Mach Intell. 1982 Feb;4(2):124-8. doi: 10.1109/tpami.1982.4767216.
A method for efficiently generating a rational model of a wide-sense stationary time series is presented. In this method the autoregressive parameters associated with an ARMA model consisting of q zeros and p poles are optimally chosen with the selection being based on a finite set of time series observations. This selection is made so that a set of Yule-Walker equation approximations are ``best'' satisfied. The resultant autoregressive parameter estimates have the desired statistical feature of being unbiased and consistent. This estimation method has been found to provide a modeling performance which typically equals or exceeds that of contemporary alternatives. Moreover, this method is amenable to a computationally efficient adaptive solution procedure. The autoregressive parameters characterizing the resultant ARMA model estimate can serve the role of decision variables in pattern classification schemes. For example, these parameters can be utilized in determining whether or not a member(s) of a given signal class is contained within a noise corrupted measurement signal. This approach has been found to be particularly effective in Doppler radar and array processing applications in which one is looking for the presence of spectral lines (i.e., sinusoids) in the measurement signal.
提出了一种有效生成宽平稳时间序列有理模型的方法。在该方法中，与包含 q 个零点和 p 个极点的 ARMA 模型相关的自回归参数是通过基于有限数量的时间序列观测进行最优选择的。这种选择是为了使一组 Yule-Walker 方程近似“最佳”满足。所得的自回归参数估计具有无偏和一致的所需统计特性。已经发现这种估计方法提供了通常等于或超过当代替代方法的建模性能。此外，该方法适用于计算上有效的自适应求解过程。表征所得 ARMA 模型估计的自回归参数可以作为决策变量用于模式分类方案。例如，这些参数可用于确定给定信号类的成员是否包含在噪声污染的测量信号中。已经发现，这种方法在多普勒雷达和阵列处理应用中特别有效，其中正在测量信号中寻找谱线（即正弦波）的存在。
