Estimation of target trajectory from passive sonar bearings and frequency measurements in the presence of multivariate normally distributed noise, with unknown inhomogeneous general covariance, is modeled as a nonlinear multiresponse parameter estimation problem. It is shown that maximum likelihood estimation in this case is identical to optimizing a determinant criterion which has a concise form and contains no elements of unknown covariance matrix. A Gauss-Newton type algorithm using only the first-order derivatives of the model function and a new convergence criterion, is presented to implement such estimation. The simulation results demonstrate that performance of the maximum likelihood estimation method with the above noise model is superior to that with the traditional noise assumption.
Passive target tracking using maximum likelihood estimation
IEEE Transactions on Aerospace and Electronic Systems ; 32 , 4 ; 1348-1354
01.10.1996
840762 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Passive Target Tracking Using Maximum Likelihood Estimation
Online Contents | 1996
|Comments on "Passive Target Tracking Using Maximum Likelihood Estimation"
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