Multi-Parameters Estimation of Coherently Distributed Sources Under Coexistence of Circular and Noncircular Signals

Tian, Ye; Yue, Hong; Rong, Xiaoliu

doi:10.1109/lcomm.2020.2981028

Cited by 15 publications

(8 citation statements)

References 14 publications

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“…is the probability density function of angular spread, where θ k and σ θ k are the nominal DOA and angular spread of the kth CD source, respectively. It is further assumed that the CD source is located in small angle scattering environment (i.e., {σ θ k } K k=1 are sufficiently small) [4][5][6][7][8][9] and follows Gaussian distribution, hence ρ(θ k , σ θ k ) is given by…”

Section: Signal Modelmentioning

confidence: 99%

“…In this section, some numerical examples are carried out to demonstrate the effectiveness of the proposed method. The MUSIC in [4] and the MpECD-SR in [5] are selected as compared methods. The number of ULA sensors is 10, and two BPSK modulated CD source signals are considered.…”

Section: Numerical Examplesmentioning

confidence: 99%

“…For an ID source, the signal components from different rays are supposed uncorrelated, whereas for a CD source, these components are correlated. In recent years, several direction finding methods for distributed sources have also been proposed successively, examples including the multiple signal classification (MUSIC), multi-parameter estimation approach in sparse representation framework (MpECD-SR), generalized distributed signal parameter estimator (CCCS-DSPE) based algorithms for CD sources [4][5][6] and the covariance matching estimator (COMET), estimation of signal parameters via rotational invariance technique (ESPRIT), and Beamspace based algorithms for ID sources [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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Direction Finding for Coherently Distributed Sources With Gain-Phase Errors

Tian¹,

Dong²,

Liu³

2021

PIER Letters

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Affected by multipath propagation as well as the receiving conditions of an actual array, a distributed source model considering array uncertainties/errors is more consistent with the realistic scenarios. In this paper, a new direction finding method for coherently distributed (CD) sources in the presence of array gain-phase errors is proposed. By exploiting partly calibrated uniform linear arrays (ULA), the gain-phase errors are first estimated according to the relationship of elements of array covariance matrix, and then a two-step iterative procedure is introduced to achieve a joint estimation of nominal DOA and angular spread from sparse recovery perspective. Performance analysis and related Cramér-Rao bound (CRB) are also provided. Numerical examples show that the proposed method can provide improved resolution and estimation accuracy, and performs almost independently of gain-phase errors.

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Section: Signal Modelmentioning

confidence: 99%

Section: Numerical Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Direction Finding for Coherently Distributed Sources With Gain-Phase Errors

Tian¹,

Dong²,

Liu³

2021

PIER Letters

Self Cite

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show abstract

“…The source signals are assumed to be a mixture of circular and noncircular ones, mutually uncorrelated. The first N nc ones are noncircular Gaussian distributed with zero mean, while the remaining N nc ones are circular Gaussian distributed with zero mean (such information can be available and has been used by some algorithms, e.g., [46]). The noise is assumed to be spatially uncorrelated, circular Gaussian distributed with zero mean, with its power denoted by σ 2 .…”

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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“…CD sources correspond to slowly time-varying channels, whereas ID sources correspond to rapidly time-varying channels. For DOA estimation of CD sources, both subspace based and sparse reconstruction based algorithms have been proposed by extending the approaches based on the point source model [19]- [23]. Although some of the above algorithms are designed for 1-D DOA estimation of CD sources, they can be easily extended to the 2-D case.…”

mentioning

confidence: 99%