Application of Manifold Separation to Parametric Localization for Incoherently Distributed Sources

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: first, we use the MST to derive a closed-form expression for the ID signal covariance matrix that is applicable to the case with arbitrary array geometries or large angular spreads; second, we find that the two-dimensional spatial spectrum can be computed efficiently by using the discrete Fourier transform algorithms coupled with the unweighted (or Gaussian-weighted) moving average for the uniformly (or Gaussian) distributed ID sources; finally, we employ the first-order Taylor expansion to formulate a weighted least-squares approach that 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.