Components Separation Algorithm for Localization and Classification of Mixed Near-Field and Far-Field Sources in Multipath Propagation

Molaei, Amir Masoud; Zakeri, Bijan; Andargoli, Seyed Mehdi Hosseini

doi:10.1109/tsp.2019.2961226

Cited by 30 publications

(27 citation statements)

References 41 publications

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“…Direction of arrival (DOA) estimation plays a key role in modern signal processing for various applications such as radar, sonar, microphone arrays, wireless communications, electronic surveillance, medical diagnosis and treatment, radio astronomy and seismology [10]. Numerous DOA estimation techniques for classical single-input multiple-output (SIMO) setup have been reported in the literature [11][12][13][14][15]. In recent years, however, the development of MIMO systems has opened up new opportunities in DOA estimation.…”

Section: Introductionmentioning

confidence: 99%

“…}, one can reach a different output. In fact, the proper design and selection of the sensors number to calculate the correlation between them is the most important factor in defining a cumulant so that Applicable for the passive scenario, no reflectivity estimates [12] Spatial cumulants (with the aim of separating non-coherent and coherent components) Applicable for the passive scenario, no reflectivity estimates [21] LASSO LASSO cannot be solved efficiently, DOA estimates only [23] Spatial cumulants; ESPRIT-like estimator Applicable for the passive scenario, no reflectivity estimates [24] SOS Applicable for the passive scenario, no reflectivity estimates [22] Spatial/temporal full ESPRIT-like approach Applicable for the passive scenario, no reflectivity estimates [25] 2D-MUSIC processing Very high computational complexity, no reflectivity estimates [26] Cross-covariance matrices between the output data blocks Applicable for the bistatic system, no reflectivity estimates [27] Covariance matrix Iterative approach with high computational cost, applicable for the passive scenario, no reflectivity estimates [ the desired output is obtained and the information is sufficient to the end of the algorithm. For this purpose, a new special cumulant is defined in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Joint Estimation of DOA, Range and Reflectivity in Near-Field by Using Mixed-Order Statistics and a Symmetric MIMO Array

Molaei

Hougne

Fusco

et al. 2022

IEEE Trans. Veh. Technol.

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Most works in the literature on parameter estimation with multiple-input multiple-output (MIMO) systems are limited to estimating only the direction of arrival (DOA), and/or based on the far-field assumption. However, the joint estimation of DOA and range is an important challenge for near-field (NF) applications. In addition, backscatter coefficient estimation, as one of the most important parameters of radar systems, has been neglected in existing works. In this paper, the joint estimation of the DOA, range and reflectivity is presented for a MIMO array in the NF. By employing the symmetry property in the array, first, two special spatial expressions based on fourth-order and second-order statistics from the received data are defined so that the output of the first expression lacks targets' range information. By collecting the information of all the sensors and calculating all the interactions between the transmit and receive sensors based on the first expression, a spatial cumulant matrix is constructed. In two different approaches, with/without the use of eigendecomposition, the virtual steering vectors are extracted. Next, DOAs are estimated by a one-dimensional (1D) spectral search. The second expression, which contains both angle and range information, is used to construct several covariance submatrices and then to construct a covariance matrix with a size corresponding to the array's dimensions. Then, corresponding ranges are estimated by employing estimated DOAs, eigendecomposition and 1D spectral searches. Finally, the equations required to estimate the reflectivity are derived. The simulation results show the remarkable performance of the proposed method in terms of computational time and estimation accuracy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Joint Estimation of DOA, Range and Reflectivity in Near-Field by Using Mixed-Order Statistics and a Symmetric MIMO Array

Molaei

Hougne

Fusco

et al. 2022

IEEE Trans. Veh. Technol.

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“…Some algorithms based on nested array for mixed sources localization have been developed. For example, using convolution neural networks to localization mixed sources [25], using exact spatial propagation geometry localization mixed sources [26], and components separation for localization and classification of mixed sources [27]. e methods mentioned above all used general nested arrays.…”

Section: Introductionmentioning

confidence: 99%

A Novel Sparse Array for Localization of Mixed Near-Field and Far-Field Sources

Wang

Cui

et al. 2021

International Journal of Antennas and Propagation

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As we all know, nested array can obtain a larger array aperture and more degrees of freedom using fewer sensors. In this study, we not only designed an enhanced symmetric nested array (ESNA), which achieved more consecutive lags and more unique lags compared with a generalized nested array but also developed a special cumulant matrix, in the case of a given number of sensors, which can automatically generate the largest consecutive lags of the array. First, the direction-of-arrivals (DOAs) of mixed sources are estimated using the special cumulant matrix. Then, we can estimate the range of the near-field source in the mixed source using a one-dimensional spectral search through estimated DOAs, and in the mixed sources, the near-field and far-field sources are classified by bringing in the range parameter. The largest consecutive lags and composition method of ESNA are also given, under a given number of sensors.Our algorithm has moderate computation complexity, which provides a higher resolution and improves the parameters’ estimation accuracy. Numerical simulation results demonstrate that the proposed array showed an outstanding performance under estimation accuracy and resolution ability for both DOA and range estimation compared with existing arrays of the same physical array sensors.

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“…The DOA estimation and localization have noticed an increase in research conducted in the case of mixed uncorrelated far-field sources (FFSs) and near-field sources (NFSs) in recent years. [10][11][12][13][14] The CRBs corresponding to a uniform linear array (ULA) have been extracted in Reference 15 for the problem of mixed uncorrelated FFSs and NFSs. On the other hand, in practical wireless communications [16][17][18] and smart jamming scenarios, received signals may be correlated.…”

Section: Introductionmentioning

confidence: 99%

“…In some practical applications, such as seismic exploration, speaker localization using a microphone array, guidance systems, and electronic supervision, any source can be located in the NF or FF. The DOA estimation and localization have noticed an increase in research conducted in the case of mixed uncorrelated far‐field sources (FFSs) and near‐field sources (NFSs) in recent years 10‐14 . The CRBs corresponding to a uniform linear array (ULA) have been extracted in Reference 15 for the problem of mixed uncorrelated FFSs and NFSs.…”

Section: Introductionmentioning

confidence: 99%

Derivation of multidimensional stochastic Cramer‐Rao bound for mixture of noncoherent and coherent near/far field signals

Molaei

Hoseinzade

2020

Trans Emerging Tel Tech

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The existing Cramer-Rao bound (CRB) expressions in the literature are not applicable to the problem of localization of mixed near-field (NF) and far-field (FF) sources, including noncoherent and coherent signals, using two-dimensional arrays. Given the unique properties of circular arrays, in this article, the mathematical model of received signals in multipath environments, including a mixture of noncoherent and coherent NF/FF signals, is introduced. Then, the stochastic CRBs associated with the above problem are derived. The compact closed-form expression of CRB extracted in this article can be used for the algorithms under the above scenario as a benchmark to evaluate the estimation accuracy of azimuth DOA, elevation DOA, and range parameters as well as fading coefficient parameter. The simulation results show that the CRBs of the azimuth DOAs, elevation DOAs, ranges, and FCs decrease, on average, at a rate of approximately O(M −1.9 ), O(M −1.9 ), O(M −1.2 ), and O(M −1.8 ) as M (number of sensors) goes to infinity, respectively, where O is the notation of the asymptotic growth rate.

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Components Separation Algorithm for Localization and Classification of Mixed Near-Field and Far-Field Sources in Multipath Propagation

Cited by 30 publications

References 41 publications

Efficient Joint Estimation of DOA, Range and Reflectivity in Near-Field by Using Mixed-Order Statistics and a Symmetric MIMO Array

Efficient Joint Estimation of DOA, Range and Reflectivity in Near-Field by Using Mixed-Order Statistics and a Symmetric MIMO Array

A Novel Sparse Array for Localization of Mixed Near-Field and Far-Field Sources

Derivation of multidimensional stochastic Cramer‐Rao bound for mixture of noncoherent and coherent near/far field signals

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