Sensor Networks TDOA Self-Calibration: 2D Complexity Analysis and Solutions

TL;DR

This paper analyzes the complexity of 2D TDOA self-calibration in sensor networks and introduces algebraic solutions that improve initialization for non-linear optimization, enhancing robustness to noise.

Contribution

It provides the first algebraic solutions for two challenging TDOA calibration scenarios and demonstrates their effectiveness as initializers for robust non-linear optimization.

Findings

Algebraic solutions successfully address previously unsolved calibration scenarios.

Proposed methods improve initialization quality for non-linear algorithms.

Results show enhanced robustness to noise in sensor network calibration.

Abstract

Given a network of receivers and transmitters, the process of determining their positions from measured pseudoranges is known as network self-calibration. In this paper we consider 2D networks with synchronized receivers but unsynchronized transmitters and the corresponding calibration techniques, known as Time-Difference-Of-Arrival (TDOA) techniques. Despite previous work, TDOA self-calibration is computationally challenging. Iterative algorithms are very sensitive to the initialization, causing convergence issues. In this paper, we present a novel approach, which gives an algebraic solution to two previously unsolved scenarios. We also demonstrate that our solvers produce an excellent initial value for non-linear optimisation algorithms, leading to a full pipeline robust to noise.