# Sparsity-Promoting Sensor Selection for Non-linear Measurement Models

arXiv: https://arxiv.org/abs/1310.5251 · HTML: https://mlsanity.com/paper/1310.5251/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2013.

## Built on by (most cited first, 3 of 3)

- [Submodular Information Selection for Hypothesis Testing with Misclassification Penalties](https://mlsanity.com/paper/2405.10930/) (2024)
- [Lazy FSCA for Unsupervised Variable Selection](https://mlsanity.com/paper/2103.02687/) (2021)
- [Robust Information Selection for Hypothesis Testing with Misclassification Penalties](https://mlsanity.com/paper/2502.14738/) (2025)

## Abstract (arXiv)

Sensor selection is an important design problem in large-scale sensor networks. Sensor selection can be interpreted as the problem of selecting the best subset of sensors that guarantees a certain estimation performance. We focus on observations that are related to a general non-linear model. The proposed framework is valid as long as the observations are independent, and its likelihood satisfies the regularity conditions. We use several functions of the Cram'er-Rao bound (CRB) as a performance measure. We formulate the sensor selection problem as the design of a selection vector, which in its original form is a nonconvex l0-(quasi) norm optimization problem. We present relaxed sensor selection solvers that can be efficiently solved in polynomial time. We also propose a projected subgradient algorithm that is attractive for large-scale problems and also show how the algorithm can be easily distributed. The proposed framework is illustrated with a number of examples related to sensor placement design for localization.