On the Characterization and Limits of 4D Radar for Aided Inertial Navigation

TL;DR

This paper analyzes FMCW radar measurement models and develops a factor graph-based estimator, demonstrating improved robustness and accuracy for aided inertial navigation in diverse environments.

Contribution

It provides a fundamental analysis of FMCW radar sensing and introduces a reliable estimator with validated simulation and experimental results.

Findings

Simulation shows the importance of different noise sources.

The estimator outperforms traditional methods in robustness and accuracy.

Experiments confirm the validity of the first-order noise approximation.

Abstract

Frequency Modulated Continuous Wave (FMCW) radar is a promising sensor for aided inertial navigation, due to its robustness in environments that challenge traditional alternatives, such as LiDAR and vision. However, its widespread adoption is hindered by complex, noisy measurements, which make reliable estimation difficult. This manuscript addresses these challenges by analyzing the fundamental measurement relations of FMCW radar sensing and developing a reliable estimator. Noise models are derived by applying first principles to the underlying signal processing of a typical radar sensor. These models guide the design of a factor graph-based estimator, utilizing a first-order approximation for the measurement noise propagation. The approach is first examined through simulation, evaluating the significance of different noise sources, the validity of the first-order approximation, and the state-dependent nature of the covariance expressions. Extensive experiments demonstrate the superior robustness and accuracy of the proposed method across diverse field environments and flight profiles, including beyond the radar's standard operating range. Furthermore, the experiments confirm the insights from the simulation regarding the behavior and performance of different estimator configurations relative to their operating conditions. The evaluation data and estimator implementation are made available at https://github.com/ntnu-arl/rig.