Precision of direction of arrival (DOA) estimation using novel three dimensional array geometries

Poormohammad, Sarah; Farzaneh, Forouhar

doi:10.1016/j.aeue.2017.02.013

Cited by 24 publications

(21 citation statements)

References 16 publications

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“…Consider there are D uncorrelated signal and interferences arriving from D different directions at angles

θ_{i}

and

φ_{i}

and received by an array of M elements with their corresponding weights. The output of the array in matrix form can be given in the following [11]:

y_{k} = {\bar{w}}^{T} \cdot \overset{false¯}{bold-italicx} ()(, k)

where

\begin{matrix} right leftthickmathspace.5em \\ \bar{x} (k) & = [\begin{matrix} \bar{a} (θ_{1}, φ_{1}) & \bar{a} (θ_{2}, φ_{2}) & \dots & \bar{a} (θ_{D}, φ_{D}) \end{matrix}] \cdot [\begin{matrix} {bold-italics}_{1} (k) \\ {bold-italicI}_{1} (k) \\ ⋮ \\ {bold-italicI}_{D - 1} (k) \end{matrix}] \\ + \bar{n} (k) = \overset{boldfalse¯}{bold-italicA} \cdot \bar{s} (k) + \bar{n} (k) \end{matrix}

And the array weights are

\overset{false¯}{bold-italicw} = [\begin{matrix} {bold-italicw}_{1} & {bold-italicw}_{2} & \dots & bold-... \end{matrix}]

…”

Section: Data Model For Music Direction Finding Algorithm and Mmse mentioning

confidence: 99%

“…It is shown that the prisms with triangular and star cross‐sections, with equal volume and equal number of elements, gave a better performance in terms of SIR with respect to other cylindrical geometries. Another novel geometry presented here is the 3D antenna array with rotated cross‐sectional configuration [11]. In [11], it was shown that the rotated 3D structures had a better performance in terms of root mean square error (RMSE) in direction finding.…”

Section: Introductionmentioning

confidence: 99%

“…Another novel geometry presented here is the 3D antenna array with rotated cross‐sectional configuration [11]. In [11], it was shown that the rotated 3D structures had a better performance in terms of root mean square error (RMSE) in direction finding. It is shown here that the rotated geometries have a better performance with respect to the conventional geometries, in terms of SIR as well.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Proposed 2D and 3D geometries intended for smart antenna applications, including direction finding and beamforming implementation

Poormohammad¹,

Farzaneh²

2019

IET Radar, Sonar & Navigation

Self Cite

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“…Consider there are D uncorrelated signal and interferences arriving from D different directions at angles

θ_{i}

and

φ_{i}

and received by an array of M elements with their corresponding weights. The output of the array in matrix form can be given in the following [11]:

y_{k} = {\bar{w}}^{T} \cdot \overset{false¯}{bold-italicx} ()(, k)

where

\begin{matrix} right leftthickmathspace.5em \\ \bar{x} (k) & = [\begin{matrix} \bar{a} (θ_{1}, φ_{1}) & \bar{a} (θ_{2}, φ_{2}) & \dots & \bar{a} (θ_{D}, φ_{D}) \end{matrix}] \cdot [\begin{matrix} {bold-italics}_{1} (k) \\ {bold-italicI}_{1} (k) \\ ⋮ \\ {bold-italicI}_{D - 1} (k) \end{matrix}] \\ + \bar{n} (k) = \overset{boldfalse¯}{bold-italicA} \cdot \bar{s} (k) + \bar{n} (k) \end{matrix}

And the array weights are

\overset{false¯}{bold-italicw} = [\begin{matrix} {bold-italicw}_{1} & {bold-italicw}_{2} & \dots & bold-... \end{matrix}]

…”

Section: Data Model For Music Direction Finding Algorithm and Mmse mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Proposed 2D and 3D geometries intended for smart antenna applications, including direction finding and beamforming implementation

Poormohammad¹,

Farzaneh²

2019

IET Radar, Sonar & Navigation

Self Cite

View full text Add to dashboard Cite

“…Xia et al proposed that cubic arrays still have ambiguity problem and the spherical array can significantly reduce this problem [22]. Recently, 3D antenna array configurations have attracted much more research interest in array signal processing [22,23,24,25,26,27,28]. Most of those 3D arrays above are constructed from regular structure (i.e., cubic, cylinder), extending the planar array (i.e., URA, UCA) or configuring the virtual 3D array based on the planar array.…”

Section: Introductionmentioning

confidence: 99%

A Sensor-Driven Analysis of Distributed Direction Finding Systems Based on UAV Swarms

Chen

Yeh

Chamberland

et al. 2019

Sensors

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This paper reports on the research of factors that impact the accuracy and efficiency of an unmanned aerial vehicle (UAV) based radio frequency (RF) and microwave data collection system. The swarming UAVs (agents) can be utilized to create micro-UAV swarm-based (MUSB) aperiodic antenna arrays that reduce angle ambiguity and improve convergence in sub-space direction-of-arrival (DOA) techniques. A mathematical data model is addressed in this paper to demonstrate fundamental properties of MUSB antenna arrays and study the performance of the data collection system framework. The Cramer–Rao bound (CRB) associated with two-dimensional (2D) DOAs of sources in the presence of sensor gain and phase coefficient is derived. The single-source case is studied in detail. The vector-space of emitters is exploited and the iterative-MUSIC (multiple signal classification) algorithm is created to estimate 2D DOAs of emitters. Numerical examples and practical measurements are provided to demonstrate the feasibility of the proposed MUSB data collection system framework using iterative-MUSIC algorithm and benchmark theoretical expectations.

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“…Many high‐resolution techniques have been proposed in the literature over the past decades. Among them, the most popular techniques are the Capon algorithm and the subspace‐based approaches such as Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) and MUltiple SIgnal Classification (MUSIC) . While Capon algorithm suffers from computationally heavy matrix inversion, ESPRIT and MUSIC algorithms suffer from high complexity due to singular value decomposition or eigenvalue decomposition (EVD).…”

Section: Introductionmentioning

confidence: 99%

Extended reduced‐rank joint estimation of direction of arrival with mutual coupling for coherent signals

Thet

Kachroo

Özdemir

2019

Trans Emerging Tel Tech

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This paper proposes an extended method for direction-of-arrival (DOA) estimation with unknown mutual coupling for coherent signals. The proposed method employs the forward/backward spatial smoothing to decorrelate the coherent signals in the process of rank reduction with the joint iterative subspace optimization approach. Then, the autocalibration of mutual coupling is performed based on the Capon's minimum variance criterion. Performance of the proposed method is compared with the state of the art algorithms in terms of DOA root-mean-square error and in terms of probability of successful estimation, where successful estimation is the case when the average of absolute DOA estimation error is within 2.5 • . It is shown that the proposed method has a similar performance as the state of the art autocalibration methods while offering a lower computational complexity.Trans Emerging Tel Tech. 2019;30:e3620.wileyonlinelibrary.com/journal/ett

show abstract

Precision of direction of arrival (DOA) estimation using novel three dimensional array geometries

Cited by 24 publications

References 16 publications

Proposed 2D and 3D geometries intended for smart antenna applications, including direction finding and beamforming implementation

Proposed 2D and 3D geometries intended for smart antenna applications, including direction finding and beamforming implementation

A Sensor-Driven Analysis of Distributed Direction Finding Systems Based on UAV Swarms

Extended reduced‐rank joint estimation of direction of arrival with mutual coupling for coherent signals

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