The DOI matches its Crossref record.
Investigating Cost-effective RF-based Detection of Drones
Abstract
Beyond their benign uses, civilian drones have increasingly been used in problematic ways that have stirred concern from the public and authorities. While many anti-drone systems have been proposed to take them down, such systems often rely on a fundamental assumption that the presence of the drone has already been detected and is known to the defender. However, there is a lack of an automated cost-effective drone detection system. In this paper, we investigate a drone detection system that is designed tao autonomously detect and characterize drones using radio frequency wireless signals. In particular, two technical approaches are proposed. The first approach is active tracking where the system sends a radio signal and then listens for its reflected component. The second approach is passive listening where it receives, extracts, and then analyzes observed wireless signal. We perform a set of preliminary experiments to explore the feasibility of the approaches using WARP and USRP software-defined platforms. Our preliminary results illustrate the feasibility of the proposed system and identify the challenges for future research.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Security analysis of drones systems: Attacks, limitations, and recommendations
Yaacoub, J.-P., Noura, H., Salman, O., & Chehab, A. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100218 · Cited by 727
Machine Learning-Based Drone Detection and Classification: State-of-the-Art in Research
Taha, B., & Shoufan, A. (2019) · IEEE Access · doi:10.1109/access.2019.2942944 · Cited by 375
Survey on Anti-Drone Systems: Components, Designs, and Challenges
Park, S., Kim, H. T., Lee, S., Joo, H., & Kim, H. (2021) · IEEE Access · doi:10.1109/access.2021.3065926 · Cited by 325
Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Technologies
Lykou, G., Moustakas, D., & Gritzalis, D. (2020) · Sensors · doi:10.3390/s20123537 · Cited by 268
A UAV Detection Algorithm Based on an Artificial Neural Network
Zhang, H., Cao, C., Xu, L., & Gulliver, T. A. (2018) · IEEE Access · doi:10.1109/access.2018.2831911 · Cited by 93
Metadata
- Authors
- Nguyen, P., Ravindranatha, M., Nguyen, A., Han, R., & Vu, T.
- Venue
- Proceedings of the 2nd Workshop on Micro Aerial Vehicle Networks, Systems, and Applications for Civilian Use
- Publisher
- ACM
- Published
- 2016-06-26
- Pages
- 17-22
- OpenAlex
- W2464322612
- Cited by
- 193
- References
- 0
- FWCI
- 8.53
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Investigating Cost-effective RF-based Detection of Drones. https://app.cochat.ai/papers/10.1145/2935620.2935632
- The paper
- Nguyen, P., Ravindranatha, M., Nguyen, A., Han, R., & Vu, T. (2016). Investigating Cost-effective RF-based Detection of Drones. Proceedings of the 2nd Workshop on Micro Aerial Vehicle Networks, Systems, and Applications for Civilian Use. https://doi.org/10.1145/2935620.2935632
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.