2014
DOI: 10.1109/joe.2013.2266195
|View full text |Cite
|
Sign up to set email alerts
|

Gain and Phase Autocalibration of Large Uniform Rectangular Arrays for Underwater 3-D Sonar Imaging Systems

Abstract: In this paper, a new calibration method for gain and phase errors in large uniform rectangle arrays is proposed for underwater 3-D sonar imaging systems. It requires only one calibrator source at an unknown position in the far field. An efficient and speedy three-step-iteration algorithm is performed first to provide a robust direction-of-arrival (DOA) estimator in the presence of gain and phase errors. It then follows with the gain and phase errors estimation using a spatial matched filter. Finally, a maximum… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
14
0

Year Published

2016
2016
2022
2022

Publication Types

Select...
6
1
1

Relationship

0
8

Authors

Journals

citations
Cited by 20 publications
(14 citation statements)
references
References 31 publications
0
14
0
Order By: Relevance
“…We assume that the N ground UEs are randomly distributed in a square serving area of D×D m 2 , with four vertices at horizontal locations of (0, y s ), (0, y s + D), (D, y s ), and (D, y s + D). A half-wavelength spacing is assumed among adjacent antennas/elements at the RIS and AP, then the achievable LoS channels modeled in angular domain are given as [26] h r,n = L r,n e r r,n (β r r,n , γ r r,n ), ∀n, (…”
Section: Simulation Resultsmentioning
confidence: 99%
“…We assume that the N ground UEs are randomly distributed in a square serving area of D×D m 2 , with four vertices at horizontal locations of (0, y s ), (0, y s + D), (D, y s ), and (D, y s + D). A half-wavelength spacing is assumed among adjacent antennas/elements at the RIS and AP, then the achievable LoS channels modeled in angular domain are given as [26] h r,n = L r,n e r r,n (β r r,n , γ r r,n ), ∀n, (…”
Section: Simulation Resultsmentioning
confidence: 99%
“…Let sp(n), n=1,2,,L, be the source waveforms. Similar to [33], the received signal vector of the non-ULA at the n th snapshot can then be expressed as, x(n)=p=1PΓa(θp,r0p)sp(n)+v(n)=ΓAs(n)+v(n)n=1,2,,L, where bold-italicx(n)=[x0(n),x1(n),,xM-1(n)]T is the measurement signal vector;v(n)=[v0(n),v1(n),,vM-1(n)]T is an independent and identically distributed complex circular zero-mean Gaussian random vector with covariance matrix σ2IM, while boldIM is the M -dimensional identity matrix;bold-italicA=[bold-italica(θ1,r01),bold-italica(θ2,r02),,bold-italica(θP,r0P)] is the nominal M×P steering matrix, the p -th column is …”
Section: Signal Modelmentioning
confidence: 99%
“…Let , , be the source waveforms. Similar to [ 33 ], the received signal vector of the non-ULA at the n th snapshot can then be expressed as, where is the measurement signal vector; is an independent and identically distributed complex circular zero-mean Gaussian random vector with covariance matrix , while is the M -dimensional identity matrix; is the nominal steering matrix, the p -th column is where is the wavelength of the p th source and denotes distance of the p th signal source to m th sensor, , . is the relative distance between and , which can be derived by geometrical relationship, To simplify the notation, we write into .…”
Section: Signal Modelmentioning
confidence: 99%
“…Direction-of-arrival (DOA) estimation is a major research issue in array signal processing and has been widely used in radar, sonar, navigation and wireless communication [1], [2]. Many subspace-based algorithms including MUSIC (Multiple Signal Classification) [3] and ESPRIT (Estimation of Signal Parameters by Rotational Invariance Techniques) [4] have the disadvantage of rank loss of the signal covariance matrix in cases where the signals are coherent.…”
Section: Introductionmentioning
confidence: 99%