# ISEE.U: Distributed online active target localization with unpredictable targets

arXiv: https://arxiv.org/abs/2210.09107 · HTML: https://mlsanity.com/paper/2210.09107/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 37 references that are in the map.
- First posted on arXiv 17 Oct 2022.

## Builds on (most cited first, 5 of 5)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Distributed Estimation with Information-Seeking Control in Agent Network](https://mlsanity.com/paper/1408.3732/) (2014)
- [Distributed, simple and stable network localization](https://mlsanity.com/paper/1406.5223/) (2014)
- [STRONG: Synchronous and asynchronous RObust Network localization, under Non-Gaussian noise](https://mlsanity.com/paper/2110.00594/) (2021)
- [Robust Localization with Bounded Noise: Creating a Superset of the Possible Target Positions via Linear-Fractional Representations](https://mlsanity.com/paper/2110.02752/) (2021)

## Abstract (arXiv)

This paper addresses target localization with an online active learning algorithm defined by distributed, simple and fast computations at each node, with no parameters to tune and where the estimate of the target position at each agent is asymptotically equal in expectation to the centralized maximum-likelihood estimator. ISEE.U takes noisy distances at each agent and finds a control that maximizes localization accuracy. We do not assume specific target dynamics and, thus, our method is robust when facing unpredictable targets. Each agent computes the control that maximizes overall target position accuracy via a local estimate of the Fisher Information Matrix. We compared the proposed method with a state of the art algorithm outperforming it when the target movements do not follow a prescribed trajectory, with x100 less computation time, even when our method is running in one central CPU.