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

TL;DR

This paper introduces ISEE.U, a distributed online active learning algorithm for target localization that is robust to unpredictable target movements, computationally efficient, and asymptotically matches centralized maximum-likelihood estimates.

Contribution

The paper presents ISEE.U, a novel distributed active localization method that requires no parameter tuning, is computationally efficient, and robust to unpredictable target dynamics.

Findings

Outperforms state-of-the-art algorithms in unpredictable target scenarios

Achieves 100 times less computation time than centralized methods

Provides asymptotically unbiased estimates matching centralized maximum-likelihood

Abstract

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.