Array Design Based on the Worst-Case Cramér-Rao Bound to Account for Multiple Targets

Conference Paper (2023)
Author(s)

C.A. Kokke (TU Delft - Signal Processing Systems)

Mario Coutiño Minguez (Netherlands Defence Academy)

R. Heusdens (TU Delft - Signal Processing Systems, Netherlands Defence Academy)

GJT Leus (TU Delft - Signal Processing Systems)

Research Group
Signal Processing Systems
DOI related publication
https://doi.org/10.1109/IEEECONF59524.2023.10476966
More Info
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Publication Year
2023
Language
English
Research Group
Signal Processing Systems
Pages (from-to)
1174-1178
ISBN (print)
979-8-3503-2575-1
ISBN (electronic)
979-8-3503-2574-4
Reuse Rights

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Abstract

Sensor selection is a useful method to help reduce computational, hardware, and power requirements while maintaining acceptable performance. Although minimizing the Cramér-Rao bound has been adopted previously for sparse sensing, it did not consider multiple targets and unknown target directions. We propose to tackle the sensor selection problem for direction of arrival estimation using the worst-case Cramér-Rao bound of two uncorrelated equal power sources on planar arrays. We cast the problem as a convex semi-definite program and retrieve the binary selection by randomized rounding. We illustrate the proposed method through numerical examples related to planar arrays. We show that our method selects a combination of edge and center elements, which contrasts with solutions obtained by minimizing the single-target Cramér-Rao bound.

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