Localization of Near-Field Sources Based on Linear Prediction and Oblique Projection Operator

Zuo, Weiliang; Xin, Jingmin; Liu, Wenyi; Zheng, Nanning; Ohmori, Hitoshi; Sano, Akira

doi:10.1109/tsp.2018.2883034

Cited by 41 publications

(21 citation statements)

References 97 publications

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“…Furthermore, there are 2(M + N + 1) equality constraints on these matrix/scalar variables. When (M + N + 1) d, the flop count in each Newton step is approximately 6(M + N + 1) 4 .…”

Section: Complexity Analysismentioning

confidence: 99%

“…During the deployment of powerful location-based applications such as enhanced 911 services, asset management, and workflow automation, wireless location is a classical problem and has attracted intensive research recently [1][2][3][4][5][6]. Technically, wireless location can be based on a variety of different kinds of measurements, such as range [7], angle [8], energy [9], visible light [10,11], or fingerprinting [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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A Semidefinite Relaxation Method for Elliptical Location

2020

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Wireless location is a supporting technology in many application scenarios of wireless communication systems. Recently, an increasing number of studies have been conducted on range-based elliptical location in a variety of backgrounds. Specifically, the design and implementation of position estimators are of great significance. The difficulties arising from implementing a maximum likelihood estimator for elliptical location come from the nonconvexity of the negative log-likelihood functions. The need for computational efficiency further enhances the difficulties. Traditional algorithms suffer from the problems of high computational cost and low initialization justifiability. On the other hand, existing closed-form solutions are sensitive to the measurement noise levels. We recognize that the root of these drawbacks lies in an oversimplified linear approximation of the nonconvex model, and accordingly design a maximum likelihood estimator through semidefinite relaxation for elliptical location. We relax the elliptical location problems to semidefinite programs, which can be solved efficiently with interior-point methods. Additionally, we theoretically analyze the complexity of the proposed algorithm. Finally, we design and carry out a series of simulation experiments, showing that the proposed algorithm outperforms several widely used closed-form solutions at a wide range of noise levels. Extensive results under extreme noise conditions verify the deployability of the algorithm.

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“…Furthermore, there are 2(M + N + 1) equality constraints on these matrix/scalar variables. When (M + N + 1) d, the flop count in each Newton step is approximately 6(M + N + 1) 4 .…”

Section: Complexity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Semidefinite Relaxation Method for Elliptical Location

2020

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“…Obviously, the dominant terms cλ 2 π P S2 cos 2 θ of (16) and (22) are equal, implying that the near-field DOA CRB is barely affected by the power profile for ranges that are not too small. To go further, we look into the second-order term (in 1/r 2 ) in (16) and (22). For example for θ = 0, we get:…”

Section: Constant Gain Vs Range and Angle-dependent Gainmentioning

confidence: 99%

“…This latter makes use of the second-order Taylor expansion of the time delay parameter, with constant amplitude gain however. Numerous methods have used these approximations, such as a polynomial rooting approach [16], an high-order ESPRIT algorithm [17], a weighted linear prediction method [18], an ESPRIT/MUSIC procedure exploiting subarrays [19], a two-stage MUSIC algorithm [20], a least-square procedure [21], a prediction and oblique projection operator method [22] and many other approaches. Furthermore, these approximations facilitate the CRB derivations (see e.g., [23]).…”

Section: Introductionmentioning

confidence: 99%

Improved localization of near-field sources using a realistic signal propagation model and optimally-placed sensors

Delmas

Gazzah

Korso

2019

Digital Signal Processing

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In this paper we analyze the estimation of the angle and the range of a narrow-band source located in the near-field of an arbitrary centro-symmetric linear array (CSLA). This analysis deals with the Cramer Rao bound (CRB) on both angle and range, obtained thanks to an exact expression of the source-to-sensor delay and a realistic (range-dependent) model of source-to-sensor attenuation, ultimately achieving two objectives. On the first hand, closed-form approximate expressions of the CRB are developed and compared to those obtained assuming (unrealistically) that sensors perceive the same power despite being at different distances from the source. While the impact on angle estimation is negligible, range CRB significantly decreases if one incorporates the more appropriate range-dependent power model (except for sources at broadsides). An important consequence is that localization algorithms taking this range-dependent modelization of the apparent source power into account in their signal modeling should have much better range performance. On the second hand, the obtained CRBs are used to design nonuniform CSLA taking into account the ambiguities, with improved angle and range estimation, comparatively to uniform linear arrays (ULA). Finally, we show that our optimized CSLA for a single source also brings some benefits for two closelyspaced sources.

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“…In [20], a novel localization algorithm via cumulant matrix reconstruction for mixed sources scenario was proposed, it avoids DOA search for NF sources and achieves a more reasonable classification of the source types. [21] investigates the localization of multiple near-field narrowband sources with a symmetric uniform linear array, and a new linear prediction approach based on the truncated singular value decomposition (LPATS) was proposed by taking an advantage of the anti-diagonal elements of the noiseless array covariance matrix. By exploiting the noncircular information of the signals, [22] proposed a novel localization method for mixed NF and FF sources using a symmetric uniform linear array (ULA).…”

Section: Introductionmentioning

confidence: 99%

Mixed Far-Field and Near-Field Source Localization Using a Linear Electromagnetic-Vector-Sensor Array With Gain/Phase Uncertainties

Tao

Kang

2021

IEEE Access

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This paper investigates the problem of mixed far-field (FF) and near-field (NF) source localization using a linear electromagnetic-vector-sensor array with gain/phase uncertainties. Firstly, several special fourth-order cumulant matrices are constructed, such that the shift invariance structure in the cumulant domain can be derived to estimate the DOA and polarization angles of each source at two electromagnetic vector sensors (EMVSs). Then, by computing the determinant of the coefficient matrix, the sources types can be classified with the prior knowledge of the number of both the FF and NF sources. On this basis, the range of NF sources and the DOAs of mixed sources at the phase reference point are captured subsequently. Finally, these estimates can be employed to generate the unknown gain/phase errors. Compared to the existing methods, the proposed one exploits both the spatial and polarization information of sources and provides a satisfactory parameters estimation performance under unknown phase/gain responses. Moreover, it does not need to perform any spectral search and not impose restriction on EMVSs placement, as well as realizes a more reasonable classification of the signal types. Simulations are carried out to verify the effectiveness of the proposed method.

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Localization of Near-Field Sources Based on Linear Prediction and Oblique Projection Operator

Cited by 41 publications

References 97 publications

A Semidefinite Relaxation Method for Elliptical Location

A Semidefinite Relaxation Method for Elliptical Location

Improved localization of near-field sources using a realistic signal propagation model and optimally-placed sensors

Mixed Far-Field and Near-Field Source Localization Using a Linear Electromagnetic-Vector-Sensor Array With Gain/Phase Uncertainties

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