| Title | Detection of LSSUAV using hash fingerprint based SVDD |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Shi, Z., Huang, M., Zhao, C., Huang, L., Du, X., Zhao, Y. |
| Conference Name | 2017 IEEE International Conference on Communications (ICC) |
| Keywords | Acoustic Fingerprints, Ad hoc networks, autonomous aerial vehicles, composability, computation complexity, feature extraction, fingerprint identification, hash fingerprint, hash fingerprint based SVDD, Human Behavior, IEEE 802.11n Standard, LSSUAV, novel distance-based support vector data description algorithm, Protocols, pubcrawl, radar detection, Resiliency, Signal to noise ratio, SVDD recognition, UAV detection, unmanned aerial vehicles |
| 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. |
| DOI | 10.1109/ICC.2017.7996844 |
| Citation Key | shi_detection_2017 |
Detection of LSSUAV using hash fingerprint based SVDD