Localization of mixed far-field and near-field sources under unknown mutual coupling

Xie, Jian; Tao, Haihong; Rao, Xuan; Su, Jia

doi:10.1016/j.dsp.2015.10.012

Cited by 34 publications

(29 citation statements)

References 34 publications

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“…It is noteworthy that the proposed algorithm woks in a similar way as that defined in [28], [29]. However, the algorithm in [28] only solves the problem of pure FF signals, whereas the proposed algorithm aims to deal with the problem of mixed FF and NF signals.…”

Section: Far-field Sources Doa Estimationmentioning

confidence: 99%

See 1 more Smart Citation

Classification and Localization of Mixed Sources Using Uniform Circular Array under Unknown Mutual Coupling

Wang¹,

Wang²,

Xie³

et al. 2019

RADIOENGINEERING

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In this paper, the authors propose an effective classification and localization algorithm of mixed far-field and near-field sources using a uniform circular array under an unknown mutual coupling. In practice, the assumption of an ideal receiving sensor array is rarely satisfied. The effects of unknown mutual coupling would degrade the performance of most high resolution algorithms. Firstly, according to rank reduction type method, the direction of arrival of far-field sources is estimated directly without mutual coupling elimination. Then, these estimates are adopted to reconstruct the mutual coupling matrix. Finally, both direction and range parameters of near-field sources are obtained through MUSIC search after mutual coupling effects and far-field components elimination. The proposed algorithm only requires the second order cumulant and any three dimensional spectrum search is circumvented. Some simulation results would prove that the proposed algorithm can reduce more than eighty percent estimating error of mixed sources localization compared to those algorithms without mutual coupling compensation.

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Section: Far-field Sources Doa Estimationmentioning

confidence: 99%

“…In [25], a method based on FOC has been presented to deal with the problem of aperture loss. Another popular type of methods against mutual coupling effect is based on the RERA type algorithms [25][26][27][28][29]. These methods take full advantage of the array aperture.…”

Section: Introductionmentioning

confidence: 99%

Classification and Localization of Mixed Sources Using Uniform Circular Array under Unknown Mutual Coupling

Wang¹,

Wang²,

Xie³

et al. 2019

RADIOENGINEERING

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show abstract

“…Based on [10], Liu and Sun presented another GESPRIT-based algorithm to alleviate the array aperture and obtain a reasonable classification result [16]. Our early works on mixed localization focused on unknown source numbers [17] and unknown mutual coupling [18].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, mixed source localization problem has been an important research topic in array signal processing [14][15][16][17][18][19]. A two-stage MUSIC (TSMUSIC) algorithm [14] was first advanced to localize mixed FF and NF sources.…”

Section: Introductionmentioning

confidence: 99%

Mixed Far-Field and Near-Field Source Localization Algorithm via Sparse Subarrays

Song

Tao

Xie

et al. 2018

International Journal of Antennas and Propagation

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Based on a dual-size shift invariance sparse linear array, this paper presents a novel algorithm for the localization of mixed far-field and near-field sources. First, by constructing a cumulant matrix with only direction-of-arrival (DOA) information, the proposed algorithm decouples the DOA estimation from the range estimation. The cumulant-domain quarter-wavelength invariance yields unambiguous estimates of DOAs, which are then used as coarse references to disambiguate the phase ambiguities in fine estimates induced from the larger spatial invariance. Then, based on the estimated DOAs, another cumulant matrix is derived and decoupled to generate unambiguous and cyclically ambiguous estimates of range parameter. According to the coarse range estimation, the types of sources can be identified and the unambiguous fine range estimates of NF sources are obtained after disambiguation. Compared with some existing algorithms, the proposed algorithm enjoys extended array aperture and higher estimation accuracy. Simulation results are given to validate the performance of the proposed algorithm.

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“…Lately, some computationally simple second-order statistics-based methods have been proposed in the literature, such as the cyclic-based method [13], the weighted linear prediction method [14], the generalized ESPRIT method [15][16], the CRB-Based method [17][18] ,the NF-DSPE and the JADSSE method [19] etc. Recently, mixed near-field and far-field sources localization are proposed [20][21]. Significantly, most of the aforementioned methods rely on the narrowband assumption, therefore, to deal with wideband signals, e.g., acoustic or seismic, in [22][23] the authors provided some schemes to solve the problem of joint range and DOA estimation for multiple wideband near field sources.…”

Section: Introductionmentioning

confidence: 99%

Direction-of-arrival and Range Estimation of Wideband Sources in the Near Field

Lin

Xie

et al. 2017

MATEC Web Conf.

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Abstract. In this letter, we address the problem of wideband sources localization in the near field of the sensor array. It is shown that the spatial sampling rate can be considered as a function of temporal frequency so that wideband sources can be represented in the spatial frequency domain. A computationally efficient localization method is then developed based on the Toeplitz matrices constructed from the frequency-dependent cross-correlation between sensors. This allows us to resolve near field sources with quite a few numbers of sensors. Indeed, the sources can be located with fewer sensors. Finally, the simulation results are presented to validate the performance of the proposed method.

show abstract

Localization of mixed far-field and near-field sources under unknown mutual coupling

Cited by 34 publications

References 34 publications

Classification and Localization of Mixed Sources Using Uniform Circular Array under Unknown Mutual Coupling

Classification and Localization of Mixed Sources Using Uniform Circular Array under Unknown Mutual Coupling

Mixed Far-Field and Near-Field Source Localization Algorithm via Sparse Subarrays

Direction-of-arrival and Range Estimation of Wideband Sources in the Near Field

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