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

Authors

• Costas A. Kokke
  • vCard
  • [email protected]
• Mario Coutino
• Laura Anitori
• Richard Heusdens
• Geert Leus

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.

Awards

A two-page abstract of this work has been awarded with the Best Paper Award by the Interdisciplinary Centre for Security, Reliability and Trust at the International Symposium on Computational Sensing in Luxembourg on the 13th of June, 2023.

Published

2023-06-04

Conference

2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Publisher

IEEE

Links

• Google Scholar
• IEEE Xplore
• Code Repository