Node-Availability-Aware Cooperative RF Identification for Low-Altitude UAVs with Distributed Multi-Receiver Sensing

This study addresses cooperative radio-frequency (RF) identification of low-altitude unmanned aerial vehicles (UAVs) under dynamic receiver availability, where single-receiver observations are susceptible to spatial variability and distributed sensing nodes may become unavailable. First, an event-level cooperative sensing architecture comprising three distributed edge RF sensing nodes and a central fusion service is developed. A unified event identifier associates observations acquired at different spatial locations, while a binary availability mask represents receiver outages, communication timeouts, and acquisition failures. Second, a node-availability-aware cross-receiver fusion framework is proposed. A shared MobileNet-1D encoder, receiver-position embeddings, and availability-masked cross-receiver multi-head attention are employed to learn complementary representations from spatial observations. During training, Random Node Dropout exposes the model to variable available-receiver sets, enabling event-level inference with three, two, or one receiver inputs. In the reported historical three-fold closed-set evaluation comprising 720 acquisition events, the Random-Node-Dropout-trained configuration achieved mean event-level identification accuracies of 98.19%, 95.14%, and 84.63% with all three receivers, any two receivers, and any single receiver, respectively. The prototype also implements an operational pipeline for multi-node acquisition, event aggregation, fusion inference, and online alerting. In a separately evaluated low-bandwidth edge-feature upload mode, the three-receiver communication payload per round decreased from 91.553 MiB to 22.5 KiB, while end-to-end latency decreased from 2226.27 to 282.21 ms. These results characterize identification performance under different receiver-input configurations and demonstrate the engineering feasibility of the distributed sensing prototype under the reported evaluation conditions.

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Publication Details

Journal
Drones
Published
2026-10-30
DOI
https://doi.org/10.3390/drones10110813
Primary Topic
Wireless Signal Modulation Classification
Type
article
Field-Weighted Citation Impact
0.00

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article

Node-Availability-Aware Cooperative RF Identification for Low-Altitude UAVs with Distributed Multi-Receiver Sensing

Juzhen Wang, Hongyu Zou, Meiyu Wang, Cheng Quan
2 links
Drones
Wireless Signal Modulation Classification
article

Node-Availability-Aware Cooperative RF Identification for Low-Altitude UAVs with Distributed Multi-Receiver Sensing

Juzhen Wang, Hongyu Zou, Meiyu Wang, Cheng Quan
article en

Abstract

This study addresses cooperative radio-frequency (RF) identification of low-altitude unmanned aerial vehicles (UAVs) under dynamic receiver availability, where single-receiver observations are susceptible to spatial variability and distributed sensing nodes may become unavailable. First, an event-level cooperative sensing architecture comprising three distributed edge RF sensing nodes and a central fusion service is developed. A unified event identifier associates observations acquired at different spatial locations, while a binary availability mask represents receiver outages, communication timeouts, and acquisition failures. Second, a node-availability-aware cross-receiver fusion framework is proposed. A shared MobileNet-1D encoder, receiver-position embeddings, and availability-masked cross-receiver multi-head attention are employed to learn complementary representations from spatial observations. During training, Random Node Dropout exposes the model to variable available-receiver sets, enabling event-level inference with three, two, or one receiver inputs. In the reported historical three-fold closed-set evaluation comprising 720 acquisition events, the Random-Node-Dropout-trained configuration achieved mean event-level identification accuracies of 98.19%, 95.14%, and 84.63% with all three receivers, any two receivers, and any single receiver, respectively. The prototype also implements an operational pipeline for multi-node acquisition, event aggregation, fusion inference, and online alerting. In a separately evaluated low-bandwidth edge-feature upload mode, the three-receiver communication payload per round decreased from 91.553 MiB to 22.5 KiB, while end-to-end latency decreased from 2226.27 to 282.21 ms. These results characterize identification performance under different receiver-input configurations and demonstrate the engineering feasibility of the distributed sensing prototype under the reported evaluation conditions.

DronesVol. 10(11)
Hangzhou Dianzi University (CN)
National Natural Science Foundation of China
Industry, innovation and infrastructure
Openalex Percentile: Top 14%
Wireless Signal Modulation Classification
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