Iterative RNDOP-Optimal Anchor Placement for Beyond Convex Hull ToA-based Localization: Performance Bounds and Heuristic Algorithms

TL;DR

This paper introduces a novel approach for optimal anchor placement in ToA-based localization outside the convex hull, proposing RNDOP as a new metric and heuristic algorithms for practical implementation.

Contribution

It develops a new RNDOP metric, formulates the anchor placement as a min-max problem, and proposes iterative heuristic schemes for robust localization outside the convex hull.

Findings

RNDOP effectively characterizes worst-case DOP in beyond convex hull scenarios.

Heuristic schemes achieve near-optimal placement with reduced complexity.

The algorithms improve localization accuracy and robustness in practical settings.

Abstract

Localizing targets outside the anchors' convex hull is an understudied but prevalent scenario in vehicle-centric, UAV-based, and self-localization applications. Considering such scenarios, this paper studies the optimal anchor placement problem for Time-of-Arrival (ToA)-based localization schemes such that the worst-case Dilution of Precision (DOP) is minimized. Building on prior results on DOP scaling laws for beyond convex hull ToA-based localization, we propose a novel metric termed the Range-Normalized DOP (RNDOP). We show that the worst-case DOP-optimal anchor placement problem simplifies to a min-max RNDOP-optimal anchor placement problem. Unfortunately, this formulation results in a non-convex and intractable problem under realistic constraints. To overcome this, we propose iterative anchor addition schemes, which result in a tractable albeit non-convex problem. By exploiting the structure arising from the resultant rank-1 update, we devise three heuristic schemes with varying performance-complexity tradeoffs. In addition, we also derive the upper and lower bounds for scenarios where we are placing anchors to optimize the worst-case (a) 3D positioning error and (b) 2D positioning error. We build on these results to design a cohesive iterative algorithmic framework for robust anchor placement, characterize the impact of anchor position uncertainty, and then discuss the computational complexity of the proposed schemes. Using numerical results, we validate the accuracy of our theoretical results. We also present comprehensive Monte-Carlo simulation results to compare the positioning error and execution time performance of each iterative scheme, discuss the tradeoffs, and provide valuable system design insights for beyond convex hull localization scenarios.