Deterministic Maximum Likelihood Approach for 3-D Near Field Source Localization

Kabaoglu, Nihat; Çırpan, Hakan Ali; Çekli, Erdinç; Paker, Selçuk

doi:10.1078/1434-8411-54100183

Cited by 28 publications

(27 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast to the two-stage MUSIC method in [39], where the sensor array and sources are in the same plane (i.e., jointly estimating azimuth angles and ranges for near-field sources, and azimuth angles for farfield sources), the proposed algorithm pays attention to localizing "arbitrary"-field sources in the spherical coordinates [25][26][27][28][29], i.e., jointly estimating azimuth-elevation arrival-angles and ranges for nearfield sources, and azimuth-elevation arrival-angles for far-field sources.…”

Section: Key Points and Contributionsmentioning

confidence: 99%

“…Therefore, the sensors on the x and y axes (i.e., (j, 0) and (0, m)) are selected to construct a cross array rather than rectangular array, which is helpful to simplify the phase form of near-field sources and develop search-free estimation algorithms (see Eq. (5) of [25] and Eq. (4) of [29] for details).…”

Section: Constructing a Special Cumulant Matrix Cmentioning

confidence: 99%

“…It seems that the extended three-dimensional MUSIC algorithm [4] can localize "arbitrary"-field sources without any matching operation or array aperture loss. However, it suffers from the heavy computational load due to search computation as that of the EM algorithm [25]. Can the far-field source localization algorithms be applied to near-field case?…”

Section: Main Difficulties In Localizing "Arbitrary"-field Sourcesmentioning

confidence: 99%

“…The expectation-maximization (EM) algorithm based on the maximum likelihood criterion is proposed in [25], but it requires search computation and iteration process and cannot give the estimations with closed analytic solution. In [26], Challa and Shamsunder developed a Unitary ESPRIT algorithm, in which an additional parameter pairing procedure is required.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, some algorithms [25][26][27][28][29] are presented to localize nearfield sources in the spherical coordinates (i.e., joint azimuth-elevationrange estimation problem). The expectation-maximization (EM) algorithm based on the maximum likelihood criterion is proposed in [25], but it requires search computation and iteration process and cannot give the estimations with closed analytic solution.…”

Section: Related Workmentioning

confidence: 99%

See 4 more Smart Citations

Joint Azimuth-Elevation/(-Range) Estimation of Mixed Near-Field and Far-Field Sources Using Two-Stage Separated Steering Vector-Based Algorithm

Liang¹,

Ding²,

Zeng³

et al. 2011

PIER

View full text Add to dashboard Cite

Abstract-Passive source localization has wide applications in array signal processing. In the practical applications, the observations collected by an array may be "arbitrary"-field signals, i.e., which are either mixed near-field and far-field signals or multiple near-field signals or multiple far-field signals. With a cross array, a two-stage separated steering vector-based algorithm is developed to localize "arbitrary"-field narrowband sources in the spherical coordinates. The key points of this paper are: i) different physical steering vectors of near-field and far-field sources are transformed into the virtual ones with the same form, thus linearizing the quadratic phases of near-field sources and allowing the same operations for near-field and far-field sources; ii) the virtual steering vector is separated into two parts and restored by introducing a special phase angle, and thus it is used to estimate the azimuth-elevation arrival-angles of "arbitrary"-field sources; and iii) special Hermitian matrices are constructed using the separated physical steering vector and their eigenvalue decomposition (EVD) are performed, thus the ranges of near-field sources are easily obtained from the eigenvector being corresponding to the smallest eigenvalue. The proposed algorithm can localize "arbitrary"-field sources without pairing parameters and multidimensional search. Simulation results are provided to validate the performance of the proposed method.

show abstract