Sensor Scheduling With Time, Energy, and Communication Constraints

Rusu, Cristian; Thompson, John; Robertson, Neil

doi:10.1109/tsp.2017.2773429

Cited by 29 publications

(19 citation statements)

References 46 publications

(111 reference statements)

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“…1 we show the initial sensors placement given by a random matrix A, the closest tight frame next to it with the same Frobenius norm denoted by B (8) and the closest tight frame B min (16). Notice how in the initial configuration the sensors are approximately concentrated along the bisector of In Figs. 2, 3 and 4 we show the RMSE estimation performance of sensor networks whose sensor placement is defined by different frames.…”

Section: Resultsmentioning

confidence: 99%

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On the use of tight frames for optimal sensor placement in time-difference of arrival localization

Rusu

Thompson

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper we analyze the use of tight frames for the problem of localizing a source from noisy time-difference of arrival measurements. Based on the Fisher information matrix, we show that positioning the sensor network according to a tight frame that also obeys some internal symmetries provides the best average localization accuracy. We connect our result to previous approaches from the literature and show experimentally that near optimal accuracy can also be provided by random tight frames. We also make the assumption that the sensors are not fixed but placed on mobile units and we study the problem of bringing them to a tight configuration with the minimum energy consumption. Although our results hold for any dimension, for simplicity of exposition, the numerical experiments depicted are in the two dimensional case.

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Section: Resultsmentioning

confidence: 99%

“…Broadly speaking, we can distinguish two approaches based on ideas from control theory [3], [4], [5] and methods from estimation theory [6]- [16], respectively.…”

Section: Introductionmentioning

confidence: 99%

On the use of tight frames for optimal sensor placement in time-difference of arrival localization

Rusu

Thompson

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…-Complementarily, here is proposed an ad-hoc µPMU placement method (µPP), which has been designed according to the demands of the proposed two-time scale SE scheme. To do so, following a similar rationale with already developed algorithms for sensor scheduling purposes [36], we pose a mixed integer semidefinite (MISDP) optimization problem. In particular, the objectives of the µPP problem are: (i) to ensure system observability (if needed) for the R-NESE scheme, while taking into consideration the existing measurements; (ii) to optimize the conditioning of the R-NESE problem [17], that in turn will result into more accurate state estimates and (iii) to allocate a sufficient subset of the predefined number of µPMUs in each DG region, according to the requirements raised from D-WTVSE.…”

Section: A Contributionmentioning

confidence: 99%

A regularized state estimation scheme for a robust monitoring of the distribution grid

Tsitsimelis

Antón-Haro

2020

International Journal of Electrical Power & Energy Systems

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In this paper, we propose a regularized state estimation (SE) scheme for the distribution grid. The ultimate goal is to track accurately the system state at a faster time scale according to the requirements of the new operational environment. The SE algorithm operates at two different time scales in which the set of available measurements are different. At the main time instants (every 15 minutes) the set of observations comprises SCADA measurements, pseudomeasurements and micro Phasor Measurement Units (µPMUs). In this case, we resort to a Regularized version of the Normal Equations based SE (R-NESE). In the intermediate time instants, only a reduced number of µPMU measurements is available. To circumvent observability issues, we exploit the fact that the voltage drop in adjacent buses is limited and, on that basis, a regularized weighted total variation estimation (WTVSE) problem is formulated. Then, the impact of in-line voltage regulators (IVLRs) is explicitly taken into consideration and that, forces us to decompose and solve the original SE problem for a number of smaller regions (D-WTVSE). The latter can be iteratively solved by resorting to the Alternating Direction Method of Multipliers (ADMM). Complementarily, we also present a µPMU placement method (µPP) in order to improve the conditioning of the R-NESE problem. This problem can be posed as a mixed integer semidefinite programming model (MISDP) and, thus, can be efficiently solved. The performance of the proposed scheme is numerically assessed on (mostly) a 95-bus distribution system for a number of realistic conditions of noise, load and photovoltaic generation profiles. A number of benchmarks are provided, as well.

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“…The second term exp(−j4πf R/c)/4πR represents the round-trip wave propagation model. Setting χ(x) = 1 and applying the spatial box window at each voxel in (20), the initial sensing matrixÃ ∈ C 24600×845 associated with all the candidate spatial samples can be constructed.…”

Section: A Planar Array Imagingmentioning

confidence: 99%

Sampling Design of Synthetic Volume Arrays for Three-Dimensional Microwave Imaging

Wang

Yarovoy

2018

IEEE Trans. Comput. Imaging

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In this paper, sampling design of three-dimensional (3-D) synthetic array (i.e., synthetic volume array) for microwave imaging is considered. Generally, the spatial sampling criteria for one-or two-dimensional arrays can be determined based on some narrowband/ultrawideband array theories. However, for 3-D arrays, where antennas are located in a volume instead of over a surface, these existing array theories are no longer straightforwardly applicable. To address the spatial sampling problem of 3-D arrays, we formulate it as a sensor/observation selection problem in this paper. Although some selection approaches exist and are conveniently applicable to small-scale problems, they are either less efficient or provide less optimal results for selection problems with data dimensions of hundreds or even thousands which is typical for microwave imaging. To get the (near-) optimal spatial sampling scheme for 3-D arrays, a greedy algorithm named clustered maximal projection on minimal eigenspace (CMPME) is proposed to select the most informative sampling positions based on some optimality criteria. This algorithm attempts to select the fewest sampling positions by considering an error threshold for the estimated images. Moreover, it has higher computational efficiency compared to the existing approaches. Finally, its effectiveness and selection performances are demonstrated through some imaging examples.

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Sensor Scheduling With Time, Energy, and Communication Constraints

Cited by 29 publications

References 46 publications

On the use of tight frames for optimal sensor placement in time-difference of arrival localization

On the use of tight frames for optimal sensor placement in time-difference of arrival localization

A regularized state estimation scheme for a robust monitoring of the distribution grid

Sampling Design of Synthetic Volume Arrays for Three-Dimensional Microwave Imaging

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