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PROJECT TITLE :

Application of Manifold Separation to Parametric Localization for Incoherently Distributed Sources - 2018

ABSTRACT:

By using the manifold separation technique (MST), we develop a computationally efficient nonetheless accurate estimator for localization of multiple incoherently distributed (ID) sources. In this Project, we have a tendency to have made the following main contributions: first, we use the MST to derive a closed-type expression for the ID signal covariance matrix that is applicable to the case with arbitrary array geometries or massive angular spreads; second, we find that the two-dimensional spatial spectrum will be computed efficiently by using the discrete Fourier remodel algorithms in addition to the unweighted (or Gaussian-weighted) moving average for the uniformly (or Gaussian) distributed ID sources; finally, we have a tendency to use the primary-order Taylor expansion to formulate a weighted least-squares approach which will improve the estimation performance significantly. Numerical results demonstrate that with less complexity, the proposed estimator offers better estimation performance compared with many classical estimators.


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