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Application of Manifold Separation to Parametric Localization for Incoherently Distributed Sources

Application of Manifold Separation to Parametric Localization for Incoherently Distributed Sources

ABSTRACT :

By using the manifold separation technique (MST), we develop a computationally efficient yet accurate estimator for localization of multiple incoherently distributed (ID) sources. In this paper, we have made the following main contributions: 1) we use the MST to derive a closed-form expression for the ID signal covariance matrix which is applicable to the case with arbitrary array geometries or large angular spreads; 2) we find that the 2-D spatial spectrum can be computed efficiently by using the discrete Fourier transform (DFT) algorithms coupled with the unweighted (or Gaussian-weighted) moving average for the Uniformly (or Gaussian) distributed ID sources; 3) we employ the first-order Taylor expansion to formulate a weighted least-squares approach which can improve the estimation performance significantly. Numerical results demonstrate that with less complexity, the proposed estimator offers better estimation performance compared with several classical estimators.

EXISTING SYSTEM :

The performance of the COMET-based method presented in depends on the accuracy of the preliminary estimates of the angular parameters. The estimators in are strictly limited to the single source assumption and cannot be extended to the multiple-source case.

The method in is suitable for a single source case and cannot guarantee the performance in the situation of multiple sources.

the dispersed signal parameter estimator and the weighted pseudo-subspace fitting (WPSF) method. However, the main difficulty of these estimators is the choice of the effective dimension.

PROPOSED SYSTEM :

Our simulation figures clearly demonstrate that in all examples the proposed estimator consistently enjoys the best performance among the methods tested.

The reason why our method outperforms the covariance fitting method can be explained by the following fact. Essentially the covariance method is a least-squares approach whereas ours is a weighted least-squares method.

It is worth pointing out that our proposed approach can be readily extended to other source distribution types such as Laplace distribution.

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