Mismatch of Near-Field Bearing-Range Spatial Geometry in Source-Localization by a Uniform Linear Array

Hsu, Yu‐Sheng; Wong, Kainam Thomas; Yeh, Lina

doi:10.1109/tap.2011.2163749

Cited by 29 publications

(18 citation statements)

References 42 publications

(77 reference statements)

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“…In the literature, approximate array steering vector formulations [ Hsu et al , ] are given. A commonly used approximate expression is

ã_{m}^{ap} ({normalΘ}_{i}, d_{i}) = \exp ({}, j \frac{2 π}{λ} ([], {boldr}^{T} {boldp}_{m} - \frac{1}{2 d_{i}} {\overset{boldr}{true}}^{T} {\overset{boldp}{true}}_{m}))

where

\overset{boldr}{true} = {[1 - \overset{2}{\cos} (φ_{i}) \overset{2}{\sin} (θ_{i}), 1 - \overset{2}{\sin} (φ_{i}) \overset{2}{\sin} (θ_{i}), 1 - \overset{2}{\cos} (θ_{i})]}^{T}

and

{\overset{boldp}{true}}_{m} = {[x_{m}^{2}, y_{m}^{2}, z_{m}^{2}]}^{T}

.…”

Section: Problem Description and Array Modelmentioning

confidence: 99%

“…In the literature, approximate array steering vector formulations [Hsu et al, 2011] are given. A commonly used approximate expression isã…”

Section: Radio Sciencementioning

confidence: 99%

“…Most of the studies in the literature use an approximate near‐field model [ Hsu et al , ; Korso et al , ] which deviates significantly from the exact near‐field model as the range becomes smaller than 4 λ . In this paper, exact near‐field model is employed in order to model the near‐field multipaths more accurately and a new method is proposed for direction finding and localization.…”

Section: Introductionmentioning

confidence: 99%

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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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“…In the literature, approximate array steering vector formulations [ Hsu et al , ] are given. A commonly used approximate expression is

ã_{m}^{ap} ({normalΘ}_{i}, d_{i}) = \exp ({}, j \frac{2 π}{λ} ([], {boldr}^{T} {boldp}_{m} - \frac{1}{2 d_{i}} {\overset{boldr}{true}}^{T} {\overset{boldp}{true}}_{m}))

where

\overset{boldr}{true} = {[1 - \overset{2}{\cos} (φ_{i}) \overset{2}{\sin} (θ_{i}), 1 - \overset{2}{\sin} (φ_{i}) \overset{2}{\sin} (θ_{i}), 1 - \overset{2}{\cos} (θ_{i})]}^{T}

and

{\overset{boldp}{true}}_{m} = {[x_{m}^{2}, y_{m}^{2}, z_{m}^{2}]}^{T}

.…”

Section: Problem Description and Array Modelmentioning

confidence: 99%

“…In the literature, approximate array steering vector formulations [Hsu et al, 2011] are given. A commonly used approximate expression isã…”

Section: Radio Sciencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Elbir

Tuncer

2014

Radio Science

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show abstract

“…uniform linear arrays (ULAs) [8]- [10], and introduce a model mismatch. This mismatch has recently been noticed to jeopardize the achievable positioning precision [11]. In [12] a lookup table is used for ULA model correction.…”

Section: Introductionmentioning

confidence: 99%

Spherical Wave Positioning Based on Curvature of Arrival by an Antenna Array

Zhang

Jost

Pöhlmann

et al. 2019

IEEE Wireless Commun. Lett.

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Array processing is a key technology for emerging mobile networks, especially in short to moderate range and LOS scenarios. In these scenarios, the incoming wavefront can be modeled by a spherical wave. The wavefront curvature, i.e. Curvature of Arrival (CoA), contains position information of the transmitter and is observable by an antenna array potentially asynchronous and non-coherent to the transmitter. We derive a simplified expression of the spherical wave positioning (SWP) Cramér-Rao bound (CRB) for arbitrary centro-symmetric arrays (CSAs), which provides a geometrical inference about the achievable performance. Additionally, a low complexity CoA positioning algorithm is proposed. In contrast to conventional methods, the proposed algorithm requires neither multiple anchors nor coordination between devices. It also outperforms the Fresnel approximation based SWP algorithms by overcoming the model mismatch. Therefore, the proposed CoA positioning algorithm is promising for precise positioning in future mobile networks.

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“…Recently, the research on the multiplicative noise in UWA communication has become a hot spot [16][17][18][19] because the multiplicative noise can describe many factors in practical systems such as nonlinearity, packet dropout, and frequency offset. Considering the various kinds of interference in UWA channel, it can be characterized by a system with multiplicative noise (SMN) model.…”

Section: Introductionmentioning

confidence: 99%

Symbol Estimation Algorithm for MIMO Underwater Acoustic Communication System Based on Multiplicative Noise Model

Zhang

et al. 2015

Mathematical Problems in Engineering

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The stochastic and time-varying underwater acoustic (UWA) channels are usually affected by serious multipath delays, energy loss and distortion factors, thus making the modeling and estimation of the UWA channel challenging problems in the research community. Based on the analysis of the UWA channel, the system with multiplicative noise (SMN) model is established to characterize the complicated factors such as random time-variation, nonlinearity, and energy attenuation. As to the multiple-input multiple-output (MIMO) UWA communication, the complicated SMN model is established for MIMO UWA channels; based on which, the transmitted symbols are estimated according to the optimal recursive filtering algorithm. The algorithm is derived based on the projection theorem, which is optimal in the sense of linear minimum variance, and can overcome the intersymbol interference and noise pollution efficiently. The optimal algorithm is computed recursively, which has the advantage of computation-efficiency and can track the random variation of the fast time-varying channel gain dynamically. Simulation results have validated the effectiveness of the algorithm. The model and the algorithm can be extended flexibly to certain practical problems, such as the joint channel and symbol estimation in underwater acoustic communication systems.

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Mismatch of Near-Field Bearing-Range Spatial Geometry in Source-Localization by a Uniform Linear Array

Cited by 29 publications

References 42 publications

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

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

Spherical Wave Positioning Based on Curvature of Arrival by an Antenna Array

Symbol Estimation Algorithm for MIMO Underwater Acoustic Communication System Based on Multiplicative Noise Model

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