文件列表:

Model (0, 2023-07-06)
Model\STC-CVCNN_lambda=0.01_m=5.hdf5 (19006104, 2023-07-06)
Result (0, 2023-07-06)
Result\STC-CVCNN_m=5.xlsx (12124, 2023-07-06)
Result\STC-CVCNN_m=5_3m.xlsx (5275, 2023-07-06)
STC-CVCNN_SC.py (5518, 2023-07-06)
STC-CVCNN_Test.py (6678, 2023-07-06)
STC-CVCNN_Train.py (4826, 2023-07-06)
Visualization (0, 2023-07-06)
Visualization\10classes_STC CVCNN.png (293076, 2023-07-06)
Visualization\20classes_STC CVCNN.png (455462, 2023-07-06)
Visualization\30classes_STC CVCNN.png (589280, 2023-07-06)
unit (0, 2023-07-06)
unit\__pycache__ (0, 2023-07-06)
unit\__pycache__\capsulelayers.cpython-36.pyc (7360, 2023-07-06)
unit\__pycache__\cosine_softmax.cpython-37.pyc (2014, 2023-07-06)
unit\__pycache__\siamese.cpython-37.pyc (11197, 2023-07-06)
unit\__pycache__\triplet_losses.cpython-37.pyc (4104, 2023-07-06)
unit\__pycache__\triplet_metrics.cpython-37.pyc (4376, 2023-07-06)
unit\complexnn (0, 2023-07-06)
unit\complexnn\__init__.py (799, 2023-07-06)
unit\complexnn\__pycache__ (0, 2023-07-06)
unit\complexnn\__pycache__\__init__.cpython-35.pyc (1002, 2023-07-06)
unit\complexnn\__pycache__\__init__.cpython-36.pyc (859, 2023-07-06)
unit\complexnn\__pycache__\__init__.cpython-37.pyc (863, 2023-07-06)
unit\complexnn\__pycache__\__init__.cpython-38.pyc (908, 2023-07-06)
unit\complexnn\__pycache__\bn.cpython-35.pyc (12578, 2023-07-06)
unit\complexnn\__pycache__\bn.cpython-36.pyc (11102, 2023-07-06)
unit\complexnn\__pycache__\bn.cpython-37.pyc (11069, 2023-07-06)
unit\complexnn\__pycache__\bn.cpython-38.pyc (11090, 2023-07-06)
unit\complexnn\__pycache__\conv.cpython-36.pyc (33834, 2023-07-06)
unit\complexnn\__pycache__\conv.cpython-37.pyc (33594, 2023-07-06)
unit\complexnn\__pycache__\dense.cpython-36.pyc (6618, 2023-07-06)
unit\complexnn\__pycache__\dense.cpython-37.pyc (6529, 2023-07-06)
unit\complexnn\__pycache__\fft.cpython-36.pyc (4447, 2023-07-06)
unit\complexnn\__pycache__\fft.cpython-37.pyc (4229, 2023-07-06)
unit\complexnn\__pycache__\init.cpython-36.pyc (5945, 2023-07-06)
unit\complexnn\__pycache__\init.cpython-37.pyc (5818, 2023-07-06)
... ...

# Radio-Frequency-Fingerprinting: Few-Shot-Specific-Emitter-Identification-via-Deep-Metric-Ensemble-Learning Requirements: keras=2.1.4, tf=1.14.0 Paper: http://arxiv.org/abs/2207.06592 or Y. Wang, G. Gui, Y. Lin, H. -C. Wu, C. Yuen and F. Adachi, "Few-Shot Specific Emitter Identification via Deep Metric Ensemble Learning," in IEEE Internet of Things Journal, vol. 9, no. 24, pp. 24***0-24994, 15 Dec.15, 2022, doi: 10.1109/JIOT.2022.3194967. # Change ADS-B 6000-> ADS-B 4800 (Remove the ICAO code) model weight, dataset and results are updated A brief introduction to this code: (change 6000 to 4800) 1. STC-CVCNN_Train: train feature embedding on auxiliary dataset of 90 classes, and visualization based on test dataset of 10 classes 2. STC-CVCNN_Test: train LR classifer with few-shot training dataset (1-5-10-15-20 shots), and test it on test dataset. Here, this code executes 100 times, because different few-shot training datasets have different performance. 3. STC-CVCNN_SC: feature visualization & get silhouette coefficient # New result (100 Monte Carlo simulations) # Different feature embedding with LR classifier | C-K | FS CVCNN | Softmax | Siamese | Triplet | SR2CNN | *STC | ST | SC | | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | | 10-1 | 10.00% | 41.30% | 50.60% | 75.***% | 85.04% | 87.66% | 73.35% | 85.37% | | 10-5 | 47.80% | 75.26% | 77.51% | 90.18% | 93.01% | 93.99% | 88.66% | 93.05% | | 10-10 | 69.40% | 87.48% | 83.34% | 93.00% | 94.32% | 95.28% | 92.31% | 94.04% | | 10-15 | 77.20% | 91.03% | 89.47% | 93.78% | 94.81% | 95.88% | 93.29% | 94.45% | | 10-20 | 84.30% | 93.56% | 91.47% | 94.18% | 94.82% | 96.15% | 94.34% | 94.99% | | 20-1 | 5.00% | 35.59% | 38.02% | 57.02% | ***.52% | 66.11% | 51.33% | 61.20% | | 20-5 | 38.80% | 61.73% | 57.00% | 71.27% | 76.05% | 80.01% | 71.37% | 75.50% | | 20-10 | 53.60% | 72.18% | 66.73% | 74.96% | 80.94% | 84.42% | 77.46% | 79.05% | | 20-15 | 63.25% | 76.99% | 69.78% | 77.93% | 82.94% | 86.53% | 81.06% | 81.22% | | 20-20 | 72.50% | 81.18% | 72.38% | 79.29% | 84.63% | 87.74% | 83.02% | 82.59% | | 30-1 | 3.33% | 27.68% | 28.81% | 46.18% | 51.28% | 55.94% | 41.81% | 52.04% | | 30-5 | 26.90% | 53.70% | 47.77% | 62.02% | 68.91% | 72.46% | 61.40% | 66.20% | | 30-10 | 47.40% | ***.22% | 58.30% | 67.54% | 73.33% | 77.60% | 68.96% | 71.22% | | 30-15 | 54.57% | 69.99% | 62.79% | 70.12% | 75.58% | 80.14% | 73.68% | 73.89% | | 30-20 | 63.30% | 74.04% | 65.70% | 72.62% | 77.77% | 81.37% | 76.20% | 75.52% | # STC-based feature embedding with diffferent classifiers | C-K | LR | LR-3models | LR-5models | LR-7models | KNN | RF | SVM | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | 10-1 | 87.66% | 89.07% | 90.12% | 89.84% | 21.12% | 78.21% | 86.75% | 10-5 | 93.99% | 95.22% | 95.62% | 95.54% | 91.86% | 93.81% | 93.48% | 10-10 | 95.28% | 96.35% | 96.53% | 9***8% | 93.92% | 94.70% | 94.33% | 10-15 | 95.88% | 96.97% | 97.05% | 97.06% | 94.73% | 95.19% | 94.88% | 10-20 | 96.15% | 97.23% | 97.40% | 97.40% | 95.25% | 95.37% | 95.22% | 20-1 | 66.11% | 69.33% | 69.97% | 71.17% | 22.76% | 58.72% | 65.24% | 20-5 | 80.01% | 82.77% | 83.56% | 83.96% | 73.51% | 78.29% | 77.26% | 20-10 | 84.42% | 87.61% | 87.83% | 88.19% | 80.67% | 83.33% | 81.65% | 20-15 | 86.53% | 89.95% | 90.22% | 90.52% | 83.58% | 85.41% | 84.45% | 20-20 | 87.74% | 91.41% | 91.34% | 91.63% | 85.49% | 87.13% | 86.04% | 30-1 | 55.94% | 60.40% | 60.89% | 61.69% | 21.74% | 48.27% | 54.74% | 30-5 | 72.46% | 77.12% | 77.87% | 78.35% | ***.81% | 70.42% | 68.95% | 30-10 | 77.60% | 82.28% | 82.93% | 83.63% | 72.53% | 76.01% | 74.52% | 30-15 | 80.14% | 84.85% | 85.40% | 85.88% | 75.36% | 78.57% | 77.51% | 30-20 | 81.37% | 86.36% | 86.79% | 87.48% | 77.62% | 80.19% | 79.26% # The influence of different sets of few-shot training samples (left: 10-way-shot with STC CVCNN and LR; right: 10-way-1-shot with STC CVCNN)   # Model weight and Dataset Link: https://pan.baidu.com/s/13qW5mnfgUHBvWRid2tY2MA Passwd:eogv or https://www.dropbox.com/s/ruu3qxfx69k69h0/Dataset.rar?dl=0

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