Title :
Improving threshold performance of the IQML algorithm
Author :
Read, William J L
Author_Institution :
Dept. of Nat. Defence, Defence Res. Establ., Ottawa, Ont., Canada
fDate :
9/1/2000 12:00:00 AM
Abstract :
In this article, improvements to the iterative quadratic maximum likelihood (IQML) algorithm are presented. The modifications are twofold: modification of the nontriviality constraint on the polynomial coefficient vector and the addition of a noise preprocessing step based on the eigendecomposition of the data covariance matrix. Simulation results are provided
Keywords :
Gaussian noise; array signal processing; covariance matrices; direction-of-arrival estimation; iterative methods; matrix decomposition; maximum likelihood estimation; polynomials; DOA estimation; IQML algorithm; additive Gaussian noise; array signal processing; data covariance matrix; direction-of-arrival estimation; eigendecomposition; iterative quadratic maximum likelihood algorithm; maximum likelihood estimation; noise preprocessing; polynomial coefficient vector modification; simulation results; threshold performance improvement; uniform linear array; Covariance matrix; Frequency estimation; Frequency measurement; Iterative algorithms; Maximum likelihood estimation; Noise measurement; Polynomials; Sensor arrays; Signal processing algorithms; Time measurement;
Journal_Title :
Signal Processing, IEEE Transactions on
