Mixed Sources Localization Based on Sparse Signal Reconstruction

Wang, Bo; Liu, Juanjuan; Sun, Xiaoying

doi:10.1109/lsp.2012.2204248

Cited by 111 publications

(82 citation statements)

References 12 publications

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“…Recently, several high resolution methods have been presented to resolve the parameter estimation problem for the mixed NFSs and FFSs [10][11][12][13][14][15][16][17]. A two-stage MUSIC algorithm using cumulant is presented to solve the mixed source localization [10].…”

Section: Introductionmentioning

confidence: 99%

“…By using this method, both the DOAs and ranges of sources can be estimated by the same eigen-pair of a defined space-time matrix which avoids parameter matching problems. An improved mixed source localization method based on sparse signal reconstruction is proposed in [15]. This method firstly transforms the time-domain data of array into cumulant domain data to estimate DOAs, then constructs the mixed overcomplete basis to get the sparse representation of the array output for range estimation.…”

Section: Introductionmentioning

confidence: 99%

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JRDF Algorithm for Joint Range-Doa-Frequency Estimation of Mixed Near-Field and Far-Field Sources

Liu¹,

Ma²,

Du³

2015

PIER M

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Abstract-This paper presents an effective joint range-DOA-frequency (JRDF) estimation method based on fourth-order cumulants for multiple mixed near-field sources and far-field sources impinging on a symmetric uniform linear array, named as JRDF algorithm. Making use of the proposed method, range-DOA-frequency can be effectively estimated by the same eigen-pair of a defined "information matrix" constructed by two fourth-order cumulant matrices. Compared with the related works, the proposed method can provide superior performance, such as higher estimation accuracy, without the procedure of parameter search or parameter matching. Simulation results are presented to demonstrate the efficacy of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

JRDF Algorithm for Joint Range-Doa-Frequency Estimation of Mixed Near-Field and Far-Field Sources

Liu¹,

Ma²,

Du³

2015

PIER M

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“…However, in some practical applications, the NF sources and FF sources may coexist, such as speaker localization using microphone arrays or acoustic source localization using sonar arrays [21][22][23]. If so, the above-mentioned algorithms [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], which are designed to locate the pure FF sources or pure NF sources, may fail to locate the mixed sources [21][22][23]. To solve the localization issue of the mixed NF and FF sources, recently, a two-stage MUSIC (TSM) algorithm [21], based on the HOS, is presented.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, when estimating the parameters (elevation angle and range) of the NF sources, the 1-DML algorithm [22] obtains lower azimuth angle estimation performance than the TSM algorithm [21]. In addition, Wang et al also propose a sparse signal reconstruction-based localization (SSRL) algorithm [23], and the SSRL algorithm gains better estimation performance than the TSM algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…TSM algorithm [21], 1-DML algorithm [22] and SSRL algorithm [23] are the outstanding contribution in the area of the mixed NF and FF sources localization. However, these localization algorithms [21][22][23] for the mixed NF and FF sources scenario are only based on the uniform linear array (ULA), and thus are only able to estimate 1-D parameter (azimuth angle) for the FF sources and 2-D parameters (azimuth angle and range) for the NF sources [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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Three-Dimensional Localization Algorithm for Mixed Near-Field and Far-Field Sources Based on Esprit and Music Method

Jiang¹,

Duan²,

Chen³

2013

PIER

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Abstract-A three-dimensional (3-D) source localization algorithm of joint elevation, azimuth angles and range estimation for the mixed near-field (NF) and far-field (FF) sources is presented in this paper. We first estimate the elevation angles of all mixed sources by using the generalized ESPRIT method. With the elevation angle estimates, the range parameters of all mixed sources are obtained, and then both the NF and FF sources are distinguished. Finally, with the elevation angle and range estimates, the azimuth angles of all mixed sources are acquired based on the conventional high-resolution MUSIC method. The proposed algorithm avoids parameter match operation, and requires neither a multidimensional search nor high-order statistics (HOS). Simulation and experiment results show the performance of the proposed algorithm in this paper.

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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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Mixed Sources Localization Based on Sparse Signal Reconstruction

Cited by 111 publications

References 12 publications

JRDF Algorithm for Joint Range-Doa-Frequency Estimation of Mixed Near-Field and Far-Field Sources

JRDF Algorithm for Joint Range-Doa-Frequency Estimation of Mixed Near-Field and Far-Field Sources

Three-Dimensional Localization Algorithm for Mixed Near-Field and Far-Field Sources Based on Esprit and Music Method

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

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