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Source-parameter estimation by approximate maximum likelihood and nonlinear regression

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1 Author(s)
J. Bohme ; Ruhr Univ., Bochum, Germany

Statistical properties of certain parametric methods for array processing in wave fields are investigated. Potential applications are the classic location problem in underwater acoustics and wavenumber-spectrum analysis in geophysical work. Asymptotic normality of Fourier-transformed outputs of an array of sensors is applied to define approximate likelihood functions to be maximized for source-parameter estimation. Usually, the parameters are those of the spectral-density matrix. Liggett's estimates are approximations of maximum likelihood estimates in this sense. Another possibility is to use conditional likelihood functions. As a consequence, the source parameters can be found by solving nonlinear-regression problems. Approximate solutions of the latter, which enhance certain simple estimates by some iterations related to Fisher's scoring method, compare favorably with Liggett's estimates. Key Words-Array processing, beam forming, applications in passive sonar, radar and geophysical work; parametric methods: maximum likelihood and nonlinear regression; theoretical study and numerical experiments.

Published in:

IEEE Journal of Oceanic Engineering  (Volume:10 ,  Issue: 3 )