Blind Calibration for Acoustic Vector Sensor Arrays

Ramamohan, Krishnaprasad Nambur; Chepuri, Sundeep Prabhakar; Comesaña, Daniel Fernández; Pousa, Graciano Carrillo; Leus, Geert

doi:10.1109/icassp.2018.8462035

Cited by 6 publications

(8 citation statements)

References 12 publications

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“…The first equality is based on the definition of the objective function in (6), and the second equality uses the equations in Proposition 1 with assumption that M < T . Inequality (12) holds thanks to Lemmata 1 and 2. Inequality (13) holds because of trace (Z k − Z k+1 )(Z k − Z k+1 ) H P ≥ 0 and trace Q(Z k − Z k+1 )(Z k − Z k+1 ) H ≥ 0, which result from the fact that P and Q are symmetric matrices and trace XX H = X 2 F ≥ 0.…”

Section: Discussionmentioning

confidence: 87%

Low-Rank and Row-Sparse Decomposition for Joint DOA Estimation and Distorted Sensor Detection

Huang¹,

Liu²,

So³

et al. 2022

Preprint

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Distorted sensors could occur randomly and may lead to the breakdown of a sensor array system. We consider an array model within which a small number of sensors are distorted by unknown sensor gain and phase errors. With such an array model, the problem of joint direction-of-arrival (DOA) estimation and distorted sensor detection is investigated and the problem is formulated under the framework of low-rank and row-sparse decomposition. We derive an iteratively reweighted least squares (IRLS) algorithm to solve the resulting problem in both noiseless and noisy cases. The convergence property of the IRLS algorithm is analyzed by means of the monotonicity and boundedness of the objective function. Extensive simulations are conducted regarding parameter selection, convergence speed, computational complexity, and performances of DOA estimation as well as distorted sensor detection. Even though the IRLS algorithm is slightly worse than the alternating direction method of multipliers in detecting the distorted sensors, the results show that our approach outperforms several state-of-the-art techniques in terms of convergence speed, computational cost, and DOA estimation performance.

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Section: Discussionmentioning

confidence: 87%

Low-Rank and Row-Sparse Decomposition for Joint DOA Estimation and Distorted Sensor Detection

Huang¹,

Liu²,

So³

et al. 2022

Preprint

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show abstract

“…More specifically, assuming that the received signals are Low-Pass Filtered (LPF) 1 and sampled at (at least) the Nyquist rate, following [20], [27], [29] with the same signal model used therein, the vector of sampled (baseband) signals from all the M sensors is given (for all t ∈ {1, . .…”

Section: Problem Formulationmentioning

confidence: 99%

Asymptotically Optimal Blind Calibration of Uniform Linear Sensor Arrays for Narrowband Gaussian Signals

Weiss,

Yeredor

2020

Preprint

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An asymptotically optimal blind calibration scheme of uniform linear arrays for narrowband Gaussian signals is proposed. Rather than taking the direct Maximum Likelihood (ML) approach for joint estimation of all the unknown model parameters, which leads to a multi-dimensional optimization problem with no closed-form solution, we revisit Paulraj and Kailath's (P-K's) classical approach in exploiting the special (Toeplitz) structure of the observations' covariance. However, we offer a substantial improvement over P-K's ordinary Least Squares (LS) estimates by using asymptotic approximations in order to obtain simple, non-iterative, (quasi-)linear Optimally-Weighted LS (OWLS) estimates of the sensors gains and phases offsets with asymptotically optimal weighting, based only on the empirical covariance matrix of the measurements. Moreover, we prove that our resulting estimates are also asymptotically optimal w.r.t. the raw data, and can therefore be deemed equivalent to the ML Estimates (MLE), which are otherwise obtained by joint ML estimation of all the unknown model parameters. After deriving computationally convenient expressions of the respective Cramér-Rao lower bounds, we also show that our estimates offer improved performance when applied to non-Gaussian signals (and/or noise) as quasi-MLE in a similar setting. The optimal performance of our estimates is demonstrated in simulation experiments, with a considerable improvement (reaching an order of magnitude and more) in the resulting mean squared errors w.r.t. P-K's ordinary LS estimates. We also demonstrate the improved accuracy in a multiple-sources directions-of-arrivals estimation task.

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“…Letting σ = [ σT s , σn ] T , it is easy to see that 1 T σ = 1. Based on (11), the convex optimization problem (9) for one-bit measurement simplifies to minimize…”

Section: One-bit Measurementsmentioning

confidence: 99%

“…Existing calibration methods for non-sparse arrays either exploit the Toeplitz structure of the covariance matrix related to the underlying linear array [10,11], or iteratively find DOAs and calibrate, in an alternating manner for irregular arrays [12].…”

Section: Introductionmentioning

confidence: 99%

Blind Calibration of Sparse Arrays for DOA Estimation with Analog and One-bit Measurements

Ramamohan

Chepuri

Comesaña

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, the focus is on the gain and phase calibration of sparse sensor arrays to localize more sources than the number of physical sensors. The proposed technique is a blind calibration method as it does not require any calibrator sources. Joint estimation of the gain errors, phase errors, and source directions is a complicated non-convex optimization problem, which is transformed into a convex optimization problem by exploiting the underlying algebraic structure. It is shown that the developed solver is suitable for analog as well as one-bit measurements. Numerical experiments based on sparse rulers are provided to illustrate the developed theory.

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Blind Calibration for Acoustic Vector Sensor Arrays

Cited by 6 publications

References 12 publications

Low-Rank and Row-Sparse Decomposition for Joint DOA Estimation and Distorted Sensor Detection

Low-Rank and Row-Sparse Decomposition for Joint DOA Estimation and Distorted Sensor Detection

Asymptotically Optimal Blind Calibration of Uniform Linear Sensor Arrays for Narrowband Gaussian Signals

Blind Calibration of Sparse Arrays for DOA Estimation with Analog and One-bit Measurements

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