Passive Localization of Mixed Far-Field and Near-Field Sources without Estimating the Number of Sources

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

doi:10.3390/s150203834

Cited by 25 publications

(21 citation statements)

References 37 publications

(39 reference statements)

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“…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%

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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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Section: Introductionmentioning

confidence: 99%

“…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%

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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“…However, for near-field (NF) sources, both the DOA and range parameters are required since the plane wave-front assumption is no longer valid. Although various algorithms focus on the pure FF or NF sources scenario [3][4][5][6][7], it is more realistic in many applications that FF and NF sources coexist, so that several typical solutions have been developed to solve this problem. In the mixed NF and FF sources scenario, the above mentioned algorithms may fail to distinguish and locate the mixed sources.…”

Section: Introductionmentioning

confidence: 99%

Algebraic Algorithm for Mixed Near-Field and Far-Field Sources Classification and Localization

Wang¹,

Wang²,

Zhang³

et al. 2018

PIER C

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Using uniform linear array (ULA), a passive localization algorithm is presented for mixed far-field (FF) and near-field (NF) signals scenarios. Based on the high-order cumulant (HOC) technique, a special Hermite matrix is constructed by three fourth-order cumulant matrices, which are calculated by dividing the ULA into two sub-arrays. Then, the special matrix of signals is decomposed to obtain the source subspace. According to ESPRIT algorithm, two transformation matrices of all sub-arrays can be obtained. Meanwhile, the two transformation matrixes could be used to calculate the range and angles of arrival (AOA) of NF sources, as well as AOAs of FF sources. Moreover, compared with two-stage MUSIC (TSMUSIC) and four-order cumulant MUSIC method, the proposed algorithm has higher accuracy for localisation of both FF and NF sources without any spectral search.

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“…In the past decades, numerous DOA estimation methods were presented in the near field, such as the two-dimensional MUSIC method [4], maximum likelihood method [4,5], the weighted linear prediction method [6] and higher-order-based methods [7,8,9,10,11]. However, these methods either require multidimensional search or suffer poor resolution from heavy aperture loss.…”

Section: Introductionmentioning

confidence: 99%

Compressed Symmetric Nested Arrays and Their Application for Direction-of-Arrival Estimation of Near-Field Sources

Xie

2016

Sensors

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In this paper, a new sensor array geometry, called a compressed symmetric nested array (CSNA), is designed to increase the degrees of freedom in the near field. As its name suggests, a CSNA is constructed by getting rid of some elements from two identical nested arrays. The closed form expressions are also presented for the sensor locations and the largest degrees of freedom obtainable as a function of the total number of sensors. Furthermore, a novel DOA estimation method is proposed by utilizing the CSNA in the near field. By employing this new array geometry, our method can identify more sources than sensors. Compared with other existing methods, the proposed method achieves higher resolution because of increased array aperture. Simulation results are demonstrated to verify the effectiveness of the proposed method.

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Passive Localization of Mixed Far-Field and Near-Field Sources without Estimating the Number of Sources

Cited by 25 publications

References 37 publications

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

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

Algebraic Algorithm for Mixed Near-Field and Far-Field Sources Classification and Localization

Compressed Symmetric Nested Arrays and Their Application for Direction-of-Arrival Estimation of Near-Field Sources

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