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

Conference paper (2023)

Authors

C.A. Kokke Signal Processing Systems - EEMCS
R. Heusdens Netherlands Defence Academy
G.J.T. Leus Signal Processing Systems - EEMCS
Research Group
Signal Processing Systems (EEMCS) (TU Delft)
Copyright: © 2023 C.A. Kokke, Mario Coutino, Laura Anitori, R. Heusdens, G.J.T. Leus
DOI
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https://doi.org/10.1109/ICASSP49357.2023.10094942
Sensor selection Array processing Sparse sensing Cramér-rao bound Multi-target estimation
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Published Date

2023

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

Abstract

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.