Maximum likelihood estimation of a linearly structured covariance with application to antenna array processing
- 6 January 2003
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
The author considers the maximum-likelihood (ML) estimation of the covariance of a zero-mean Gaussian random vector from its samples, when the covariance has a certain known structure that typically arises in antenna array processing. Owing to this structure, the conventional sample covariance is no longer the ML estimator. While previous approaches to ML estimation of similar structured covariances have relied on brute-force nonlinear programming methods to extremize the likelihood function, this approach derives analytical results for the special structure considered. The resulting algorithm is based on the eigendecomposition of a matrix derived from the sample covariance. The algorithm has application in the estimation of spectral parameters of correlated source signals in a wavefield.Keywords
This publication has 7 references indexed in Scilit:
- Optimum beamforming for coherent signal and interferencesIEEE Transactions on Acoustics, Speech, and Signal Processing, 1988
- Estimation of spectral parameters of correlated signals in wavefieldsSignal Processing, 1986
- Exact maximum likelihood parameter estimation of superimposed exponential signals in noiseIEEE Transactions on Acoustics, Speech, and Signal Processing, 1986
- A subspace rotation approach to signal parameter estimationProceedings of the IEEE, 1986
- Multiple emitter location and signal parameter estimationIEEE Transactions on Antennas and Propagation, 1986
- Estimation of structured covariance matricesProceedings of the IEEE, 1982
- A general method for analysis of covariance structuresBiometrika, 1970