Efficient Application of MUSIC Algorithm Under the Coexistence of Far-Field and Near-Field Sources

He, Jinhai; Swamy, M.N.S.; Ahmad, M.O.

doi:10.1109/tsp.2011.2180902

Cited by 171 publications

(228 citation statements)

References 17 publications

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“…T is a array noise vector, and a z (θ k , r k ) is the steering vector [14][15][16][17][18][19][20] represented by…”

Section: Review Of Data Model Of the Uniform Linear Arraymentioning

confidence: 99%

“…However, when a source is in the Fresnel region of the array aperture [2][3][4][5], those algorithms in [2][3][4][5][6][7][8][9][10][11] will fail to locate sources. Therefore, for the NF source localization issue, some other algorithms [12][13][14][15][16][17][18][19][20][21][22][23][24][25] are also developed. More specifically, for 2D NF sources localization, Swindlehurst and Kailath proposed a spatial Wigner distribution approach [12], however, this method has a less-than-ideal estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Later on, Huang and Barkat [13] developed a 2D MUSIC method with high resolution, but it requires 2D search and thus has huge amount of computations. In addition, some other algorithms, such as the higher order ESPRITLike algorithm [14], the subspace-based (SB) algorithm [15], the two steps (TS) algorithm [16], and the weighted linear prediction (WLP) algorithm [17] are also presented to locate the NF source.…”

Section: Introductionmentioning

confidence: 99%

“…The above-mentioned 2D [14][15][16][17] and 3D [18][19][20][21][22][23][24][25] NF source localization algorithms have one or more main constraints: i) the number of source that they can deal with at most is not more than the half of number of array sensors; ii) the inter-sensor spacing of the array is within the quarter-wavelength of sources; iii) the extra parameters pair-matching procedure is required; iv) the computational cost is large.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome the shortages of the conventional NF source localization algorithms [14][15][16][17][18][19][20][21][22][23][24][25], this paper develops a novel AE-based NF localization algorithm to jointly estimate the elevation/azimuth angle and range parameters of the 3D NF sources. Firstly, the proposed algorithm inserts some "zero" elements into the output vectorz(t) of the NLA along Z axis so as to constructs a new array output vectorz (t) which is corresponding to a ULA.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Localization of 3d Near-Field Source Using the Aperture Extension Method and Nonuniform Cross Array

Jiang¹,

Duan²,

Li³

et al. 2013

PIER B

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Abstract-Depending on the aperture extension (AE), a high performance three-dimensional (3D) near-field (NF) source localization algorithm is proposed with the nonuniform linear array (NLA). The proposed algorithm first generates some fictitious sensors to extend the array aperture by constructing a new Toeplitz matrix, and then obtains a two-dimensional (2D) covariance matrix which only contains the elevation angle and range parameters, and another 3D covariance matrix which contains the elevation/azimuth angle and range parameters. Then based on the 2D covariance matrix, both the elevation angle and range parameters are estimated by using the NLA along the Z axis. With the estimates of both the elevation angle and range parameters and combining the 3D covariance matrix, the estimates of the azimuth angle parameters are obtained using the NLA along the Y axis. The proposed algorithm has four main merits: i) unlike some classical NF source localization algorithms, the quarter-wavelength sensor spacing constraint is not required and more sources can be located simultaneously by the proposed algorithm; ii) the 3D parameters of the proposed algorithm are paired automatically; iii) the 3D search required in conventional 3D multiple signal classification (MUSIC) algorithm is replaced with only one-dimensional (1D) search, and thus the computational burden is reduced; iv) the proposed algorithm gains superior parameter estimation accuracy and resolution.

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“…T is a array noise vector, and a z (θ k , r k ) is the steering vector [14][15][16][17][18][19][20] represented by…”

Section: Review Of Data Model Of the Uniform Linear Arraymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Localization of 3d Near-Field Source Using the Aperture Extension Method and Nonuniform Cross Array

Jiang¹,

Duan²,

Li³

et al. 2013

PIER B

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Far‐field DOA estimation and near‐field localization for multipath signals

Elbir

Tuncer

2014

Radio Science

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In direction finding and localization applications, multipath signals are important sources of error for parameter estimation. When the antenna array receives multipath reflections which are coherent with the far‐field line‐of‐sight signal, estimating the far‐ and near‐field components becomes an important problem. In this paper, a new method is proposed to estimate the direction‐of‐arrival (DOA) of the far‐field source and to localize its near‐field multipaths. Far‐field source DOA is estimated using calibration of the antenna array. A near‐to‐far transformation is proposed for the estimation of the near‐field source DOA angles. In order to estimate the near‐field range parameters, a compressive sensing approach is presented where a dictionary with near‐field sources with different ranges is employed. As a result, the proposed method estimates the far‐field and near‐field source DOAs as well as the range and the signal amplitudes of the near‐field sources. This method is evaluated using close‐to‐real world data generated by a numerical electromagnetic tool, where the array and transmitter are placed in an irregular terrain and array data are generated using full 3‐D propagation model. It is shown that unknown source parameters can be estimated effectively showing the potential of the proposed approach in applications involving high‐frequency direction finding and indoor localization.

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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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Efficient Application of MUSIC Algorithm Under the Coexistence of Far-Field and Near-Field Sources

Cited by 171 publications

References 17 publications

Localization of 3d Near-Field Source Using the Aperture Extension Method and Nonuniform Cross Array

Localization of 3d Near-Field Source Using the Aperture Extension Method and Nonuniform Cross Array

Far‐field DOA estimation and near‐field localization for multipath signals

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

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