Name
Abstract | ||
|---|---|---|
With the rapid development of science and technology, unmanned aerial vehicles (UAVs) gradually become the worldwide focus of science and technology. Not only the development and application but also the security of UAV is of great significance to modern society. Different from methods using radar, optical or acoustic sensors to detect UAV, this paper proposes a novel distance-based support vector data description (SVDD) algorithm using hash fingerprint as feature. This algorithm does not need large number of training samples and its computation complexity is low. Hash fingerprint is generated by extracting features of signal preamble waveforms. Distance-based SVDD algorithm is employed to efficiently detect and recognize low, slow, small unmanned aerial vehicles (LSSUAVs) using 2.4GHz frequency band. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC) | UAV detection, hash fingerprint, SVDD recognition |
Field | DocType | ISSN |
Computer science,Real-time computing,Artificial intelligence,Wireless ad hoc network,Radar,Pattern recognition,Frequency band,Simulation,Support vector machine,Signal-to-noise ratio,Feature extraction,Fingerprint,Hash function | Conference | 1550-3607 |
Citations | PageRank | References |
1 | 0.35 | 9 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiyuan Shi | 1 | 1 | 0.35 |
| Minmin Huang | 2 | 5 | 1.78 |
| Caidan Zhao | 3 | 12 | 3.17 |
| Lianfen Huang | 4 | 132 | 32.83 |
| X. Du | 5 | 2320 | 241.73 |
| Y. Zhao | 6 | 8 | 2.49 |