空时二维线性预测谱估计算法

线性预测是时间序列分析中常用方法,针对传统一维线性预测谱估计算法只能估计信号源角度或信号频率问题,提出空时二维线性预测算法。采取对空时二维阵列接收到的数据进行数据抽取和排列,和对数据协方差矩阵进行重新构造的方法,求取空时二维线性预测权值并进行谱峰搜索。重点分析了空时二维的前向预测、后向预测和双向预测算法的原理,着重研究了构造的空时二维线性预测协方差矩阵的数据结构,讨论了前向、后向和双向预测的相互关系以及二维与一维的关系,并与空时二维最小方差算法、空时二维多重信号分类(multiple signal classification,MUSIC)算法进行了对比与分析。理论分析与仿真表明,一维空域、一维时域算法的前向、后向和双向预测为空时二维预测算法的特例,同时空时二维预测算法不仅克服了空时二维最小方差算法、空时二维MUSIC算法不能解相干信号源的缺点,还具有很好的测向测频能力。

Linear predictive spectrum estimation algorithm based on space-time two-dimensional

Linear prediction is a commonly used method in time series analysis. For the traditional one-dimensional linear prediction spectrum estimation algorithm, only the source angle or signal frequency can be estimated. A space-time two-dimensional linear prediction algorithm is proposed. The data are extracted and arranged by the data received by the space-time two-dimensional array, and the data covariance matrix is reconstructed. The space-time two-dimensional linear prediction weight is obtained and the peak search is performed. The paper focuses on the principle of space-time two-dimensional forward prediction, backward prediction and bidirectional prediction algorithm, focuses on the data structure of the constructed space-time two-dimensional linear prediction covariance matrix, and discusses the forward and backward directions. The mutual relationship between two-way prediction and the relationship between two dimension and one dimension is compared and analyzed with the space-time two-dimensional minimum variance algorithm and the space-time two-dimensional MUSIC algorithm. Theoretical analysis and simulation show that the forward, backward and bidirectional prediction of one-dimensional spatial and one-dimensional time domain algorithms are special cases of the space-time two-dimensional prediction algorithm, and the space-time two-dimensional prediction algorithm not only overcomes the shortcomings of the coherent signal source that the space-time two-dimensional minimum variance algorithm and the space-time two-dimensional MUSIC algorithm can not solve, but also has a good direction finding frequency measurement capability.