Sensor Selection using the Two-Target Cram\'er-Rao Bound for Angle of Arrival Estimation

TL;DR

This paper introduces a sensor selection method for angle of arrival estimation that minimizes the worst-case Cramér-Rao bound for two sources, optimizing sensor placement while considering multiple targets.

Contribution

It formulates a convex semi-definite programming approach for sensor selection based on the two-target Cramér-Rao bound, addressing multi-target scenarios previously unconsidered.

Findings

Selected sensors are optimally placed at array edges and center.

Method effectively reduces data and hardware requirements.

Numerical examples demonstrate improved sensor placement.

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 Cram\'er-Rao bound has been adopted previously for sparse sensing, it did not consider multiple targets and unknown source models. We propose to tackle the sensor selection problem for angle of arrival estimation using the worst-case Cram\'er-Rao bound of two uncorrelated sources. 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 selection of elements at the edges plus the center of the linear array.