# Controlled Sensing: A Myopic Fisher Information Sensor Selection Algorithm

arXiv: https://arxiv.org/abs/1408.4443 · HTML: https://mlsanity.com/paper/1408.4443/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2014.

## Built on by (most cited first, 1 of 1)

- [Dynamic programming with incomplete information to overcome navigational uncertainty in a nautical environment](https://mlsanity.com/paper/2112.14657/) (2021)

## Abstract (arXiv)

This paper considers the problem of state tracking with observation control for a particular class of dynamical systems. The system state evolution is described by a discrete-time, finite-state Markov chain, while the measurement process is characterized by a controlled multi-variate Gaussian observation model. The computational complexity of the optimal control strategy proposed in our prior work proves to be prohibitive. A suboptimal, lower complexity algorithm based on the Fisher information measure is proposed. Toward this end, the preceding measure is generalized to account for multi-valued discrete parameters and control inputs. A closed-form formula for our system model is also derived. Numerical simulations are provided for a physical activity tracking application showing the near-optimal performance of the proposed algorithm.