# Data-driven Vector-measurement-sensor Selection based on Greedy Algorithm

## TL;DR

This paper proposes an extended greedy algorithm for selecting vector-measurement-sensors to improve state reconstruction accuracy from sparse data, demonstrated on particle-image velocimetry datasets.

## Contribution

It introduces an extended greedy sensor selection method specifically for vector measurements, enhancing state estimation from limited sensor data.

## Key findings

- Effective sensor selection improves state reconstruction accuracy.
- Method applied successfully to particle-image velocimetry data.
- Demonstrates the benefit of greedy algorithms in sensor placement for vector measurements.

## Abstract

A vector-measurement-sensor problem for the least squares estimation is considered, by extending a previous novel approach in this paper. An extension of the vector-measurement-sensor selection of the greedy algorithm is proposed and is applied to particle-image-velocimetry data to reconstruct the full state based on the information given by sparse vector-measurement sensors.