Radar Fingerprint Feature Extraction Based on VMD

Lin, Fuyan; Sun, Songlin; Hou, Xiaoying; Wang, Kun

doi:10.1109/iscit.2019.8905210

“…SEI mainly focuses on the recognition of the unintentional modulation features of signals. Studies [8] and [9] have concluded that the primary signal has high energy and great impact on individual recognition. If the primary signal can be suppressed and the energy entropy of unintentional modulation features can be enhanced, the recognition will be more accurate.…”

Section: Primary Signal Phase Suppression Based On Improved Vmdmentioning

confidence: 99%

“…Compared with the primary signal of the source, the recognition rate of the fractal box dimension feature was im-proved by approximately 10%. Lin et al [9] performed Variational Mode Decomposition (VMD) on the radar pulse data and identifies the characteristics of unintentional modulation through the multi-domain features of each Intrinsic Mode Function (IMF). The VMD technique was initially proposed by Dragomiretskiy et al [10].…”

Section: Introductionmentioning

confidence: 99%

Primary signal phase suppression technique based on mutual information improved variational mode decomposition

Yue,

Lu,

Wang

et al. 2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

0

View full text Add to dashboard Cite

Specific emitter identification (SEI) identifies targets mainly by unintentional modulation of the signal. However, due to the high energy of the primary signal, once the primary signal changes, the recognition becomes less effective or even impossible using a feature database that is not updated. In this paper, we propose to use a mutual information improved variable mode decomposition (VMD) algorithm to suppress the primary signal phase of the transmitter. Furthermore, we simulate the feature extraction of the unintentional phase modulation of the transmitter signal and use support vector machine (SVM) for individual identification. The simulation results show that the algorithm improves the recognition rate by about 6% (0 dB) compared to the retained primary signal. The results demonstrate that our proposed phase suppression technique improves the adaptability and accuracy of individual identification of transmitters.

show abstract

“…SEI mainly focuses on the recognition of the unintentional modulation features of signals. Studies [8] and [9] have concluded that the primary signal has high energy and great impact on individual recognition. If the primary signal can be suppressed and the energy entropy of unintentional modulation features can be enhanced, the recognition will be more accurate.…”

Section: Primary Signal Phase Suppression Based On Improved Vmdmentioning

confidence: 99%

“…Compared with the primary signal of the source, the recognition rate of the fractal box dimension feature was improved by approximately 10%. Lin et al [9] performed variational mode decomposition (VMD) on the radar pulse data and identifies the characteristics of unintentional modulation through the multi-domain features of each intrinsic mode function (IMF). The VMD technique was initially proposed by Dragomiretskiy et al [10].…”

Section: Introductionmentioning

confidence: 99%

Primary Signal Phase Suppression Technique Based On Mutual Information Improved Variational Mode Decomposition

Yue¹,

Wang²,

Lyu³

2021

Preprint

0

View full text Add to dashboard Cite

Specific emitter identification (SEI) identifies targets mainly by unintentional modulation of the signal. However, due to the high energy of the primary signal, once the primary signal changes, the recognition becomes less effective or even impossible using a feature database that is not updated. In this paper, we propose to use a mutual information improved variable mode decomposition (VMD) algorithm to suppress the primary signal phase of the transmitter. Furthermore, we simulate the feature extraction of the unintentional phase modulation of the transmitter signal and use support vector machine (SVM) for individual identification. The simulation results show that the algorithm improves the recognition rate by about 6% (0 dB) compared to the retained primary signal. The results demonstrate that our proposed phase suppression technique improves the adaptability and accuracy of individual identification of transmitters.

show abstract