Optimal Sensor Placement for Signal Extraction

Ghayem, Fateme; Rivet, Bertrand; Jutten, Christian; Farias, Rodrigo Cabral

doi:10.1109/icassp.2019.8682432

Cited by 4 publications

(22 citation statements)

References 11 publications

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“…Recently, by assuming a stochastic model, a robust sensor placement criterion based on the maximization of the aver-age signal to noise ratio (SNR) has been proposed [11]. This method works by maximizing the average SNR of the desired source, and has shown a significant improvement compared to the classical kriging approaches in terms of the output SNR.…”

Section: Introductionmentioning

confidence: 99%

“…The rest of this paper is organized as follows: in Section 2 a review on the problem of optimal sensor placement for source extraction, as well as the criterion suggested in [11] are presented. The details of the proposed method are discussed in Section 3.…”

Section: Introductionmentioning

confidence: 99%

“…In this section we present an overview on signal extraction for optimal sensor placement suggested in [11]. Let y(x, t) denote the observation recorded at location x and at time t which is modelled as…”

Section: Introductionmentioning

confidence: 99%

“…, y(x M , t)] T . To find the best f , a classical criterion is the output signal-to-noise ratio (SNR) defined by [11] SN R(f…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, we consider a zero mean Gaussian process with covariance matrix k n (x, x ) to model the noise. The criterion to optimize is then obtained as [11]:…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Gradient-Based Algorithm with Spatial Regularization for Optimal Sensor Placement

Ghayem

Rivet

Jutten

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we are interested in optimal sensor placement for signal extraction. Recently, a new criterion based on output signal to noise ratio has been proposed for sensor placement. However, to solve the optimization problem, a greedy approach is used over a grid, which is not optimal. To improve this method, we present an optimization approach to locate all the sensors at once. We further add a constraint to the problem that controls the average distances between the sensors. To solve our problem, we use an alternating optimization penalty method. As the associated cost function is non-convex, the proposed algorithm should be carefully initialized. We propose to initialize it with the result of the greedy method. Experimental results show the superiority of the proposed method over the greedy approach.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…, y(x M , t)] T . To find the best f , a classical criterion is the output signal-to-noise ratio (SNR) defined by [11] SN R(f…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, we consider a zero mean Gaussian process with covariance matrix k n (x, x ) to model the noise. The criterion to optimize is then obtained as [11]:…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Gradient-Based Algorithm with Spatial Regularization for Optimal Sensor Placement

Ghayem

Rivet

Jutten

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

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show abstract

Optimal sensor placement for permanent magnet synchronous motor condition monitoring using a digital twin-assisted fault diagnosis approach

Kohtz,

Zhao,

Renteria

et al. 2024

Reliability Engineering & System Safety

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Robust Sensor Placement for Signal Extraction

Ghayem

Rivet

Farias

et al. 2021

IEEE Trans. Signal Process.

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This paper proposes an efficient algorithm for robust sensor placement with the purpose of recovering a source signal from noisy measurements. To model uncertainty on the spatiallyvariant sensors gain and on the spatially correlated noise, we assume that both are realizations of Gaussian processes. Since the signal to noise ratio (SNR) is also uncertain in this context, to achieve a robust signal extraction, we propose a new placement criterion based on the maximization of the probability that the SNR exceeds a given threshold. This criterion can be easily evaluated using the Gaussian process assumption. Moreover, to reduce the computational complexity of the joint maximization of the criterion with respect to all sensor positions, we suggest a sequential maximization approach, where the sensor positions are chosen one at a time. Finally, we present numerical results showing the superior robustness of the proposed approach when compared to standard sensor placement criteria aimed at interpolating the spatial gain and to a recently proposed criterion aimed at maximizing the average SNR.

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Optimal Sensor Placement for Signal Extraction

Cited by 4 publications

References 11 publications

Gradient-Based Algorithm with Spatial Regularization for Optimal Sensor Placement

Gradient-Based Algorithm with Spatial Regularization for Optimal Sensor Placement

Optimal sensor placement for permanent magnet synchronous motor condition monitoring using a digital twin-assisted fault diagnosis approach

Robust Sensor Placement for Signal Extraction

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