Active Planning for Cooperative Localization: A Fisher Information Approach

TL;DR

This paper presents an active planning approach for cooperative localization in wireless networks, where controlled anchor nodes are moved to maximize information gain and improve agent position accuracy.

Contribution

It introduces a receding horizon control method that optimizes anchor movements based on Bayesian Fisher information, enhancing localization accuracy.

Findings

Significant improvement in localization accuracy with controlled anchors.

Efficient tree-search approximations for real-time control.

Demonstrated effectiveness in a 3-D numerical case study.

Abstract

Location-aware networks will introduce new services and applications for modern convenience, surveillance, and public safety. In this paper, we consider the problem of cooperative localization in a wireless network where the position of certain anchor nodes can be controlled. We introduce an active planning method that aims at moving the anchors such that the information gain of future measurements is maximized. In the control layer of the proposed method, control inputs are calculated by minimizing the traces of approximate inverse Bayesian Fisher information matrixes (FIMs). The estimation layer computes estimates of the agent states and provides Gaussian representations of marginal posteriors of agent positions to the control layer for approximate Bayesian FIM computations. Based on a cost function that accumulates Bayesian FIM contributions over a sliding window of discrete future timesteps, a receding horizon (RH) control is performed. Approximations that make it possible to solve the resulting tree-search problem efficiently are also discussed. A numerical case study demonstrates the intelligent behavior of a single controlled anchor in a 3-D scenario and the resulting significantly improved localization accuracy.