Sensor Selection for Angle of Arrival Estimation Based on the Two-Target Cramér-Rao Bound

Sensor selection is a useful method to help reduce data throughput, as well as computational, power, and hardware requirements, while still maintaining acceptable performance. Although minimizing the Cramér-Rao bound has been adopted previously for sparse sensing, it did not consider multiple target...

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Bibliographic Details
Main Authors: Kokke, Costas A., Coutino, Mario, Anitori, Laura, Heusdens, Richard, Leus, Geert
Format: Conference Proceeding
Language:English
Subjects:
Acoustics
array processing
Array signal processing
Computational modeling
cramér-rao bound
Estimation
Hardware
multi-target estimation
sensor selection
Sensors
sparse sensing
Throughput
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Summary:Sensor selection is a useful method to help reduce data throughput, as well as computational, power, and hardware requirements, while still maintaining acceptable performance. Although minimizing the Cramér-Rao bound has been adopted previously for sparse sensing, it did not consider multiple targets and unknown source models. In this work, we propose to tackle the sensor selection problem for angle of arrival estimation using the worst-case Cramér-Rao bound of two uncorrelated sources. To do so, we cast the problem as a convex semi-definite program and retrieve the binary selection by randomized rounding. Through numerical examples related to a linear array, we illustrate the proposed method and show that it leads to the natural selection of elements at the edges plus the center of the linear array. This contrasts with the typical solutions obtained from minimizing the single-target Cramér-Rao bound.
ISSN:2379-190X
DOI:10.1109/ICASSP49357.2023.10094942