# Millimeter Wave Wireless Assisted Robot Navigation with Link State Classification

arXiv: https://arxiv.org/abs/2110.14789 · HTML: https://mlsanity.com/paper/2110.14789/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2021.

## Built on by (most cited first, 7 of 7)

- [Digital Twin-Enhanced Wireless Indoor Navigation: Achieving Efficient Environment Sensing with Zero-Shot Reinforcement Learning](https://mlsanity.com/paper/2306.06766/) (2023)
- [Reinforcement Learning with Physics-Informed Symbolic Program Priors for Zero-Shot Wireless Indoor Navigation](https://mlsanity.com/paper/2506.22365/) (2025)
- [Wireless Channel Prediction in Partially Observed Environments](https://mlsanity.com/paper/2207.00934/) (2022)
- [Channel Modeling for FR3 Upper Mid-band via Generative Adversarial Networks](https://mlsanity.com/paper/2404.17069/) (2024)
- [EM-GANSim: Real-time and Accurate EM Simulation Using Conditional GANs for 3D Indoor Scenes](https://mlsanity.com/paper/2405.17366/) (2024)
- [Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation](https://mlsanity.com/paper/2604.26787/) (2026)
- [Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition](https://mlsanity.com/paper/2604.26793/) (2026)

## Abstract (arXiv)

The millimeter wave (mmWave) bands have attracted considerable attention for high precision localization applications due to the ability to capture high angular and temporal resolution measurements. This paper explores mmWave-based positioning for a target localization problem where a fixed target broadcasts mmWave signals and a mobile robotic agent attempts to capture the signals to locate and navigate to the target. A three-stage procedure is proposed: First, the mobile agent uses tensor decomposition methods to detect the multipath channel components and estimate their parameters. Second, a machine-learning trained classifier is then used to predict the link state, meaning if the strongest path is line-of-sight (LOS) or non-LOS (NLOS). For the NLOS case, the link state predictor also determines if the strongest path arrived via one or more reflections. Third, based on the link state, the agent either follows the estimated angles or uses computer vision or other sensor to explore and map the environment. The method is demonstrated on a large dataset of indoor environments supplemented with ray tracing to simulate the wireless propagation. The path estimation and link state classification are also integrated into a state-of-the-art neural simultaneous localization and mapping (SLAM) module to augment camera and LIDAR-based navigation. It is shown that the link state classifier can successfully generalize to completely new environments outside the training set. In addition, the neural-SLAM module with the wireless path estimation and link state classifier provides rapid navigation to the target, close to a baseline that knows the target location.