Sensing-Assisted LoS/NLoS Identification in Dynamic UAV Positioning Systems

TL;DR

This paper introduces a novel sensing-assisted method for identifying line-of-sight and non-line-of-sight conditions in dynamic UAV positioning, combining multi-modal data and a dual-input neural network to improve accuracy and robustness.

Contribution

The paper presents the first sensing-assisted LoS/NLoS identification method using a multi-modal dataset and a dual-input fusion network for UAVs in urban environments.

Findings

Identification accuracy reaches 97.69%.

Method outperforms traditional CIR-only and RGB-only approaches.

Achieves strong few-shot generalization with fewer than 200 samples.

Abstract

In this paper, a sensing-assisted non-line-of-sight (NLoS) identification method for dynamic uncrewed aerial vehicle (UAV) positioning is proposed for the first time. For urban UAV-to-ground scenarios, a new multi-modal sensing-communication integrated dataset is constructed to support line-of-sight (LoS)/NLoS identification, covering two typical urban scenarios and a wide range of flight altitudes. Based on the constructed dataset, a novel dual-input feature fusion network is proposed, which addresses the challenge of heterogeneous representations between RGB images and channel impulse response (CIR) data to enable the joint extraction and fusion of sensing and communication features for LoS/NLoS identification. Simulation results show that the identification accuracy can reach up to 97.69%, while achieving an improvement of at least 3.59% compared to traditional CIR-only and RGB-only methods. Moreover, strong few-shot generalization is observed, as the proposed method stabilizes and approaches full-sample performance with fewer than 200 target samples and exceeds traditional CIR-only and RGB-only methods with fewer than 100 target samples in all cross-scenario and cross-altitude experiments. Even under Gaussian noise with a variance of 0.35 applied to RGB images, the accuracy degradation remains approximately 0.5%. By utilizing the proposed LoS/NLoS identification method, the error of trilateration positioning can be reduced by approximately 70% in a crossroad scenario, verifying the utility of the proposed method.