1994IEEE Signal Processing LettersRequires access

The two source maximum likelihood function

John E. Piper

Open publisher page 1 citations

Abstract

The maximum likelihood method is designed to yield high resolution estimates in a multiple source environment. The article derive a simplified representation of the maximum likelihood function for the two source case. This case is instructive to understand when and why the full power of the maximum likelihood method should be used. This approach extends the results of a previous paper (see J. Piper, IEEE Trans. Signal Processing, vol.42, no.2, p.412, 1994) that showed how the maximum likelihood function calculation can be transformed from a matrix problem to a vector product. The maximum likelihood function for spectral estimation is given.>

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What this paper is about

The maximum likelihood method is designed to yield high resolution estimates in a multiple source environment. The article derive a simplified representation of the maximum likelihood function for the two source case. This case is instructive to understand when and why the full power of the maximum likelihood method should be used. This approach extends the results of a previous paper (see J. Piper, IEEE Trans. Signal Processing, vol.42, no.2, p.412, 1994) that showed how the maximum likelihood function calculation can be transformed from a matrix problem to a vector product. The maximum likelihood function for spectral estimation is given.>

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Available abstract

The maximum likelihood method is designed to yield high resolution estimates in a multiple source environment. The article derive a simplified representation of the maximum likelihood function for the two source case. This case is instructive to understand when and why the full power of the maximum likelihood method should be used. This approach extends the results of a previous paper (see J. Piper, IEEE Trans. Signal Processing, vol.42, no.2, p.412, 1994) that showed how the maximum likelihood function calculation can be transformed from a matrix problem to a vector product. The maximum likelihood function for spectral estimation is given.>

Key concepts: Maximum likelihood, Computer science, Likelihood function, Function (biology), Maximum likelihood sequence estimation, Algorithm, Mathematics, Estimation theory

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