Sensor Self-Localization for Antenna Arrays Subject to Bending and Vibrations

Santori, Agnès; Barrère, Jean; Chabriel, Gilles; Jauffret, Claude; Medynski, D.

doi:10.1109/taes.2010.5461663

Cited by 10 publications

(5 citation statements)

References 13 publications

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“…To solve the phase ambiguity problem, auxiliary information is required and contributes to the selecting factor c k correctly from the candidate grids. In applying the additional information on the array structures, the phase ambiguities are solved by minimising the intersensor distance deformation in [15], that is, However, the mismatches between the estimated and nominal intersensor distances may lead to the incorrect estimation of c k . As shown in Figure 3, the constraint of the intersensor distance is equivalent to a circle in the x-O-y coordinate system, and certain ghost positions may be incorrectly selected only by minimising the intersensor distance deformation in (18).…”

Section: Phase Unwrapping Techniquementioning

confidence: 99%

Section: Phase Unwrapping Techniquementioning

confidence: 99%

“…To solve the phase ambiguity problem, auxiliary information is required and contributes to the selecting factor

c_{k}

correctly from the candidate grids. In applying the additional information on the array structures, the phase ambiguities are solved by minimising the intersensor distance deformation in [15], that is,

\begin{array}{l} {\hat{bold-italicp}}_{(l - 1) M_{c} + m} \\ = \arg \min {| ‖ p_{(l - 1) M_{c} + m}^{c} - {\hat{bold-italicp}}_{(l - 1) M_{c} + m - 1} ‖ - {\bar{d}}_{(l - 1) M_{c} + m} |}, \end{array}

where

{bold-italicp}_{(l - 1) M_{c} + m}^{c}

and

{\overset{p}{true}}_{(l - 1) M_{c} + m}

are the candidate and estimated positions of the

m th

sensor in the

l th

subarray, respectively.

{\overset{d}{true}}_{(l - 1) M_{c} + m}

is the nominal intersensor distance between the

m th

and

(m - 1) th

sensors in the

l th

subarray.…”

Section: Array Shape Calibrationmentioning

confidence: 99%

“…Figure 1 shows a deformed linear array, where the nominal sensor positions are distributed along the x ‐axis and y ‐axis. We define the position error between the true position and the nominal one of the

m th

sensor as

{normalΔ}_{m}

, and the details of the calibrations of

{normalΔ}_{m}

can be found in [5–15]. If

{normalΔ}_{m} ≪ λ

(

λ

is the wavelength), then the position error denotes only a small perturbation.…”

Section: Introductionmentioning

confidence: 99%

“…However, when the sensor position error is sufficiently large and

{normalΔ}_{m} \geq λ

, the phase ambiguity problems yield ghost positions. In [15], a constant modulus algorithm (CMA) for the calibration of bended arrays is proposed. Sensor positions are obtained from the estimated steering matrix, and phase ambiguities are simply solved by assuming the intersensor distance deformations.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Array shape calibration with phase unwrapping techniques for highly deformed arrays

Yang

2021

IET Radar Sonar & Navi

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Flexible sensor arrays, such as towed and bottom-mounted arrays, typically suffer from large shape deformations, which seriously affect the results of array processing. In this article, an innovative array shape calibration method with phase unwrapping techniques for highly deformed arrays is proposed. The deformed array is divided into several subarrays, and the first subarray is assumed to suffer from slight shape deformations which can be pre-calibrated. With the consideration of highly deformed arrays, uncalibrated sensors in other subarrays would have serious position mismatches. The phase differences between uncalibrated and pre-calibrated sensors are probably larger than 2π, which lead to phase ambiguity problems. A phase unwrapping technique is proposed to efficiently solve the phase ambiguity problem by considering the physical structure constraints of the deformed array, such as intersensor distance and bending angle. Then, sensor positions in highly deformed arrays are calibrated with multiple target directions. Simulation results evaluate the performance of the proposed calibration method, and the SwellEx-96 shallow water experiment data set is utilised to demonstrate the effectiveness of the proposed method.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Phase Unwrapping Techniquementioning

confidence: 99%

Section: Phase Unwrapping Techniquementioning

confidence: 99%

“…To solve the phase ambiguity problem, auxiliary information is required and contributes to the selecting factor