Underdetermined DOA Estimation Under the Compressive Sensing Framework: A Review

Co-array-based Direction of Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable interest in array processing thanks to its capability of providing enhanced degrees of freedom. Although the literature presents a variety of estimators in this context, none of them are proven to be statistically efficient. This work introduces a novel estimator for the co-array-based DoA estimation employing the Weighted Least Squares (WLS) method. An analytical expression for the large sample performance of the proposed estimator is derived. Then, an optimal weighting is obtained so that the asymptotic performance of the proposed WLS estimator coincides with the Cramér-Rao Bound (CRB), thereby ensuring asymptotic statistical efficiency of resulting WLS estimator. This implies that the proposed WLS estimator has a significantly better performance compared to existing methods. Numerical simulations are provided to validate the analytical derivations and corroborate the improved performance.Index Terms-Sparse linear arrays, direction of arrival (DoA) estimation, weighted least squares estimator, Cramér-Rao bound (CRB), performance analysis, asymptotically statistically efficient estimator.

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“…Finally, inserting (14) into (12), concentrates the WLS objective on DoAs and the WLS estimator of θ follows aŝ…”

Section: B Wls Estimates Of Doasmentioning

confidence: 99%

An Asymptotically Efficient Weighted Least Squares Estimator for Co-Array-Based DoA Estimation

Sedighi

Shankar

Ottersten

2020

IEEE Trans. Signal Process.

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“…Direction of arrival (DOA) estimation has been widely studied in recent years and many algorithms have been introduced to solve the problem, such as multiple signal classification (MUSIC) [1], estimation of signal parameters via rotational invariance techniques (ESPRIT) [2] and those based on sparsity or compressive sensing (CS) [3]- [5]. In its early time, most research on DOA estimation was based on omnidirectional antennas, ignoring the polarization information of impinging signals.…”

Section: Introductionmentioning

confidence: 99%

Direction Finding for a Mixture of Single-Transmission and Dual-Transmission Signals

Lan

Liu

2019

2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

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Currently, most of existing research in direction of arrival (DOA) estimation is focused on single signal transmission (SST) based signal. However, to make full use of the degree of freedom provided by the system in the polarisation domain, the dual signal transmission (DST) model has been adopted more and more widely in wireless communications. In this work, a DOA estimation method for a mixture of SST and DST signals (referred to as the mixed signal transmission (MST) model) is proposed. To our best knowledge, this is the first time to study the DOA estimation problem for such an MST model. There are two steps in the proposed method, which deals with the two kinds of signals separately. The performance of the proposed method is compared with the Cramér-Rao Bound (CRB) based on computer simulations.

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“…The compressed sensing (CS)-based methods have been proposed to estimate the directions by exploiting the signal sparsity in the spatial domain [6][7][8][9][10][11][12][13][14]. Notably, the sparse Bayesian learning (SBL) and the relevance vector machine (RVM) proposed in [15] can achieve better estimation performance in the CS-based direction finding methods, where the directions are estimated by reconstructing the sparse signals in the spatial domain with the corresponding distribution assumptions of unknown parameters.…”

Section: Introductionmentioning

confidence: 99%

SBL-Based Direction Finding Method with Imperfect Array

Chen

Zhang

et al. 2018

Electronics

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The imperfect array degrades the direction finding performance. In this paper, we 1 investigate the direction finding problem in uniform linear array (ULA) system with unknown 2 mutual coupling effect between antennas. By exploiting the target sparsity in the spatial domain, 3 sparse Bayesian learning (SBL)-based model is proposed and converts the direction finding problem 4 into a sparse reconstruction problem. In the sparse-based model, the off-grid errors are introduced 5 by discretizing the direction area into grids. Therefore, an off-grid SBL model with mutual coupling 6 vector is proposed to overcome both the mutual coupling and the off-grid effect. With the distribution 7 assumptions of unknown parameters including the noise variance, the off-grid vector, the received 8 signals and the mutual coupling vector, a novel direction finding method based on SBL with unknown 9 mutual coupling effect named DFSMC is proposed, where an expectation-maximum (EM)-based 10 step is adopted by deriving the estimation expressions for all the unknown parameters theoretically. 11Simulation results show that the proposed DFSMC method can outperform state-of-the-art direction 12 finding methods significantly in the array system with unknown mutual coupling effect. 13

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Underdetermined DOA Estimation Under the Compressive Sensing Framework: A Review

Cited by 147 publications

References 68 publications

An Asymptotically Efficient Weighted Least Squares Estimator for Co-Array-Based DoA Estimation

An Asymptotically Efficient Weighted Least Squares Estimator for Co-Array-Based DoA Estimation

Direction Finding for a Mixture of Single-Transmission and Dual-Transmission Signals

SBL-Based Direction Finding Method with Imperfect Array

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