A weighted linear prediction method for near-field source localization

Grosicki, Emmanuèle; Abed-Meraim, Karim

doi:10.1109/icassp.2002.5745269

Cited by 33 publications

(43 citation statements)

References 8 publications

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“…Most of the existing near-field sources localization techniques [ 6 , 7 , 8 , 9 , 11 , 14 ] use an approximated model, and ULA is often used in the approximated model-based methods. The approximated path differences—which are the second-order Taylor approximations of Equations ( 4 ) and ( 5 )—can respectively be written as [ 6 ]

and

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Section: Approximated Model-based Methods Proposed In [ 1mentioning

confidence: 99%

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An Exact Model-Based Method for Near-Field Sources Localization with Bistatic MIMO System

Singh

Wang

Chargé

2017

Sensors

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In this paper, we propose an exact model-based method for near-field sources localization with a bistatic multiple input, multiple output (MIMO) radar system, and compare it with an approximated model-based method. The aim of this paper is to propose an efficient way to use the exact model of the received signals of near-field sources in order to eliminate the systematic error introduced by the use of approximated model in most existing near-field sources localization techniques. The proposed method uses parallel factor (PARAFAC) decomposition to deal with the exact model. Thanks to the exact model, the proposed method has better precision and resolution than the compared approximated model-based method. The simulation results show the performance of the proposed method.

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and

…”

Section: Approximated Model-based Methods Proposed In [ 1mentioning

confidence: 99%

“…Most of the existing near-field sources localization techniques [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ] are based on an approximated model. In practice, a near-field point source has a spherical wavefront [ 6 ], which implies a nonlinear model.…”

Section: Introductionmentioning

confidence: 99%

An Exact Model-Based Method for Near-Field Sources Localization with Bistatic MIMO System

Singh

Wang

Chargé

2017

Sensors

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“…Therefore unlike most of the existing algorithm {e.g. [2–5, 16]}, the proposed algorithm can offer enhanced estimation precision of DOA and range by extending the inter‐grid spacing without adding more sensors. Remarks B Regarding the main computational complexity, our algorithm requires ( N − 1)(8 M ) 2 + (4/3)(8 M ) 3 multiplications involved in calculating the SOS and in performing the eigen‐decompositions [8]. In contrast, using the HOS‐based algorithm [4] for our estimation problem would result in 4 N (6 M ) 2 + (4 / 3)(6 M ) 3 multiplications in terms of the above two procedures.…”

Section: Algorithm Developmentmentioning

confidence: 99%

“…Near‐field source localisation with a passive sensor array is one of the issues in array signal processing fields, since the near‐field source case can often occur, for example, in sonar, electronic surveillance and seismic exploration. Many approaches have been already developed, including maximum‐likelihood method [1], two‐dimensional multiple signal classification (MUSIC) method [2, 3], estimation of signal parameters via rotational invariance techniques (ESPRIT)‐like based on high‐order statistics (HOS) [4], the weighted linear prediction method [5] etc. Unfortunately, most of these methods involve either multidimensional search or HOS, which lead to a high computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Near‐field source localisation using a velocity sensor array

Liu

Pan

2013

IET Radar, Sonar & Navigation

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This study presents a new algorithm for near-field source localisation by using a uniform linear array consisting of velocity-sensor pairs (VSPs). Two schemes are used for range and direction of arrival (DOA) estimation in the proposed algorithm. The first one exploits the acoustic velocity vector field's two Cartesian components and the triangular location relationship among the near-field sources and the VSPs. The resulting range and DOA estimates are ambiguity-free, regardless of the inter-sensor spacing. Whereas the second scheme uses the so-called electric angles to determine the ranges and DOAs. Although the derived estimates may be ambiguous in the case of the inter-sensor spacing larger than a quarter wavelength, this ambiguity can be removed by using the estimates obtained in the first scheme. Therefore the proposed algorithm can offer high estimation accuracy by increasing inter-sensor spacing (because of array aperture extension). In addition, the proposed algorithm does not require multidimensional search, pairing parameters or high-order statistics, hence, has a low computational complexity.

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“…Some studies [ 13 , 14 ] estimated the source azimuths by constructing a higher order statistics (HOS) matrix to reduce the array aperture loss. There are also some other near-field parameter estimation methods, such as, the maximum likelihood estimator proposed in [ 15 ] and the weighted linear prediction method presented in [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Sparse Reconstruction Based Robust Near-Field Source Localization Algorithm

Lin

et al. 2018

Sensors

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Non-Gaussian impulsive noise widely exists in the real world, this paper takes the α-stable distribution as the mathematical model of non-Gaussian impulsive noise and works on the joint direction-of-arrival (DOA) and range estimation problem of near-field signals in impulsive noise environment. Since the conventional algorithms based on the classical second order correlation statistics degenerate severely in the impulsive noise environment, this paper adopts two robust correlations, the fractional lower order correlation (FLOC) and the nonlinear transform correlation (NTC), and presents two related near-field localization algorithms. In our proposed algorithms, by exploring the symmetrical characteristic of the array, we construct the robust far-field approximate correlation vector in relation with the DOA only, which allows for bearing estimation based on the sparse reconstruction. With the estimated bearing, the range can consequently be obtained by the sparse reconstruction of the output of a virtual array. The proposed algorithms have the merits of good noise suppression ability, and their effectiveness is demonstrated by the computer simulation results.

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A weighted linear prediction method for near-field source localization

Cited by 33 publications

References 8 publications

An Exact Model-Based Method for Near-Field Sources Localization with Bistatic MIMO System

An Exact Model-Based Method for Near-Field Sources Localization with Bistatic MIMO System

Near‐field source localisation using a velocity sensor array

Sparse Reconstruction Based Robust Near-Field Source Localization Algorithm

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