Performance analysis of distributed source parameter estimator (DSPE) in the presence of modeling errors due to the spatial distributions of sources

On Spatial Smoothing for DOA Estimation of 2D Coherently Distributed Sources with Double Parallel Linear Arrays

“…The authors of [10] have developed an efficient DSPE algorithm and proposed a generalized beamforming estimator for CD sources in [11]. Generally, the parameters of CD sources are approximate solutions under the assumption of small angular spreads, the performance of DSPE algorithm is analyzed in [12]. All these methods are based on 1D CD source models, which assume that scatterers and arrays are in the same plane.…”

“…Supposing that signals from different CD sources are coherent, sample covariance matrices are rank defect. Thus, subspace-based algorithms [1,[6][7][8][9][10][11][12][13][14] and ESPRIT class algorithms [15,16] which are based on eigendecomposition of sample covariance matrices cannot be applied for DOA estimation of distributed sources. Subspace-based algorithms and ESPRIT class algorithms are based on decomposition of covariance matrix.…”

“…In [1,[6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23], CD sources have been regarded as uncorrelated with each other; estimators cannot be applied for CD sources which are correlated. In [24], though 1D CD sources correlated with each other are discussed, but the general model of coherent CD sources has not been presented.…”

A 2D Nested Array Based DOA Estimator for Incoherently Distributed Sources via Sparse Representation Utilizing L1-Norm

“…As to CD sources, utilizing different array configurations representative estimators have been proposed in [2][3][4][5][6][7][8][9][10], which are mostly based on rotational invariance relationship derived by Taylor approximation of generalized steering vectors. For ID sources, several algorithms have been presented and can be roughly divided into four categories: subspace-based algorithms developed from multiple signal classification (MUSIC) such as distributed signal parameter estimator (DSPE) [1] and dispersed signal parametric estimation (DSPARE) [11], generalizations of Capon's methods [12][13][14] involving high-order matrix inversions and spectral searches, the maximum likelihood (ML) approaches [15][16][17] requiring high computational complexity but with better accuracy, and covariance matching estimation techniques (COMET) [18][19][20][21] with lower computational complexity than ML approach but the same large sample behavior.…”

Estimation for Two-Dimensional Nonsymmetric Coherently Distributed Source in L-Shaped Arrays

“…In the presence of model errors, the performance of DSPE algorithm is analyzed in [8], and the performance of MUSIC is analyzed in [9]. Considering mismodeling of the spatial distribution of distributed sources, a robust estimator is proposed in [10] by means of exploiting some properties of the generalized steering vector in the case of CD sources and the covariance matrix in the case of ID sources.…”