摘要
Householder transform is used to triangularize the data matrix, which is basedon the near prediction error equation. It is proved that the sum of squared residuals for eachAR order can be obtained by the main diagonal elements of upper triangular matrix, so thecolumn by column procedure can be used to develop a recursive algorithm for AR modeling andspectral estimation. In most cases, the present algorithm yields the same results as the covariancemethod or modified covariance method does. But in some special cases where the numerical ill-conditioned problems are so serious that the covariance method and modified covariance methodfail to estimate AR spectrum, the presented algorithm still tends to keep good performance. Thetypical computational results are presented finally.
Householder transform is used to triangularize the data matrix, which is based on the near prediction error equation. It is proved that the sum of squared residuals for each AR order can be obtained by the main diagonal elements of upper triangular matrix, so the column by column procedure can be used to develop a recursive algorithm for AR modeling and spectral estimation. In most cases, the present algorithm yields the same results as the covariance method or modified covariance method does. But in some special cases where the numerical ill- conditioned problems are so serious that the covariance method and modified covariance method fail to estimate AR spectrum, the presented algorithm still tends to keep good performance. The typical computational results are presented finally.
