DOA estimation via sparse recovering from the smoothed covariance vector

Cai, Jingjing; Bao, Dan

doi:10.1109/jsee.2016.00059

Cited by 18 publications

(14 citation statements)

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“…There are two representative sparse array examples: co-prime arrays [2,3,4] and nested arrays [5,6]. Sparse arrays can form a larger aperture given the same number of antennas and more importantly provide much more degrees of freedom (DOFs) for direction of arrival (DOA) estimation than traditional uniform arrays [7,8,3,9,10,11,12]. However, to our best knowledge, the DOA estimation problem for such sparse arrays has not been properly studied yet to exploit the possible non-circularity of the impinging signals.…”

Section: Introductionmentioning

confidence: 99%

Sparse array extension for non-circular signals with subspace and compressive sensing based DOA estimation methods

et al. 2018

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The virtual array generation process based on typical sparse arrays is studied for a mixture of circular and non-circular impinging signals. It consists of two sub-arrays: one is the traditional difference co-array and the other one is the new sum co-array. The number of consecutive virtual array sensors is analysed for the nested array case, but it is difficult to give a closed-form result for a general sparse array. Based on the extended covariance matrix of the physical array, two classes of direction of arrival (DOA) estimation algorithms are then developed, with one based on the subspace method and one based on sparse representation or the compressive sensing (CS) concept. Both the consecutive and non-consecutive parts of the virtual array can be exploited by the CS-based method, while only the consecutive part can be exploited by the subspace-based one. As a result, the CS-based solution can have a better performance than the subspace-based one, though at the cost of significantly increased computational complexity. The two classes of algorithms can also deal with the special case when all the signals are noncircular. Simulation results are provided to verify the performance of the proposed algorithms.

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Section: Introductionmentioning

confidence: 99%

Sparse array extension for non-circular signals with subspace and compressive sensing based DOA estimation methods

et al. 2018

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“…According to different choices of the noncircularity rate ρ i , we have the following three types of signals. corresponding to the ith source can be expressed as [27], [28], [31] y…”

Section: Data Model a General Source Model With Arbitrary Secondmentioning

confidence: 99%

Stochastic Cramer-Rao Bound for DOA Estimation With a Mixture of Circular and Noncircular Signals

et al. 2020

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The Cramer-Rao bound (CRB) offers insights into the inherent performance benchmark of any unbiased estimator developed for a specific parametric model, which is an important tool to evaluate the performance of direction-of-arrival (DOA) estimation algorithms. In this paper, a closed-form stochastic CRB for a mixture of circular and noncircular uncorrelated Gaussian signals is derived. As a general one, it can be transformed into some existing representative results. The existence condition of the CRB is also analysed based on sparse arrays, which allows the number of signals to be more than the number of physical sensors. Finally, numerical comparisons are conducted in various scenarios to demonstrate the validity of the derived CRB. INDEX TERMS Cramer-Rao bound, direction of arrival, circular and noncircular, sparse arrays.

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“…Many examples of sparse representation applications for DOA estimation can be found in the literature. In [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ], sparse representation is applied in the data domain, often to the covariance matrix. Another popular approach to the sparse representation DOA estimation problem is to use Bayesian Learning [ 20 , 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Sparse Representation to Bartlett Spectra for Improved Direction of Arrival Estimation

Compaleo

Gupta

2020

Sensors

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A new technique for high-resolution direction of arrival estimation is presented. The method utilizes the traditional Bartlett spectra and sparse representation to locate emitters in single and multiple emitter scenarios. A method for selecting the sparse representation regularization parameter is also presented. Using Monte Carlo simulations, we show that the proposed approach achieves accurate direction of arrival (DOA) estimations that are unbiased and a variance that approaches the Cramer–Rao lower bound. We show that our method outperforms the popular MUSIC algorithm, and is slightly better than the sparse representation based L1-SVD algorithm when angular separation between emitters is small, signal SNR is low, and a small number of snapshots are used in DOA estimation.

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DOA estimation via sparse recovering from the smoothed covariance vector

Cited by 18 publications

References 0 publications

Sparse array extension for non-circular signals with subspace and compressive sensing based DOA estimation methods

Sparse array extension for non-circular signals with subspace and compressive sensing based DOA estimation methods

Stochastic Cramer-Rao Bound for DOA Estimation With a Mixture of Circular and Noncircular Signals

Application of Sparse Representation to Bartlett Spectra for Improved Direction of Arrival Estimation

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