Low complexity radar signal classification based on spectrum shape
In order to solve the problems of high computational complexity, low recognition accuracy of low signal to noise ratio (SNR) environment and low fidelity of simulation data in radar signal modulation recognition, a low complexity radar signal classification algorithm based on spectrum shape was prop...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2022-01-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022011/ |
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| _version_ | 1841529019359559680 |
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| author | Liang YIN Rui LIN Xiaolei WANG Yuliang YAO Lin ZHOU Yuan HE |
| author_facet | Liang YIN Rui LIN Xiaolei WANG Yuliang YAO Lin ZHOU Yuan HE |
| author_sort | Liang YIN |
| collection | DOAJ |
| description | In order to solve the problems of high computational complexity, low recognition accuracy of low signal to noise ratio (SNR) environment and low fidelity of simulation data in radar signal modulation recognition, a low complexity radar signal classification algorithm based on spectrum shape was proposed.Signal spectrum was normalized, feature parameters were extracted by spectrum sampling method, and then machine learning classification model was trained.The test results of the data generated by the radar signal source show that the classification accuracy of Barker code, Frank code, LFM code, BPSK, QPSK modulation and conventional radar signals is more than 90% (SNR≥3 dB).The algorithm has low computational complexity, can adapt to the change of signal parameters, and has good generalization. |
| format | Article |
| id | doaj-art-6c1331a0eb3f4241b66c95c22ace5781 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2022-01-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-6c1331a0eb3f4241b66c95c22ace57812025-01-15T03:26:30ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012022-01-0138253559808817Low complexity radar signal classification based on spectrum shapeLiang YINRui LINXiaolei WANGYuliang YAOLin ZHOUYuan HEIn order to solve the problems of high computational complexity, low recognition accuracy of low signal to noise ratio (SNR) environment and low fidelity of simulation data in radar signal modulation recognition, a low complexity radar signal classification algorithm based on spectrum shape was proposed.Signal spectrum was normalized, feature parameters were extracted by spectrum sampling method, and then machine learning classification model was trained.The test results of the data generated by the radar signal source show that the classification accuracy of Barker code, Frank code, LFM code, BPSK, QPSK modulation and conventional radar signals is more than 90% (SNR≥3 dB).The algorithm has low computational complexity, can adapt to the change of signal parameters, and has good generalization.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022011/spectrum shapelow complexityfeature extractionspectrum sampling |
| spellingShingle | Liang YIN Rui LIN Xiaolei WANG Yuliang YAO Lin ZHOU Yuan HE Low complexity radar signal classification based on spectrum shape Dianxin kexue spectrum shape low complexity feature extraction spectrum sampling |
| title | Low complexity radar signal classification based on spectrum shape |
| title_full | Low complexity radar signal classification based on spectrum shape |
| title_fullStr | Low complexity radar signal classification based on spectrum shape |
| title_full_unstemmed | Low complexity radar signal classification based on spectrum shape |
| title_short | Low complexity radar signal classification based on spectrum shape |
| title_sort | low complexity radar signal classification based on spectrum shape |
| topic | spectrum shape low complexity feature extraction spectrum sampling |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022011/ |
| work_keys_str_mv | AT liangyin lowcomplexityradarsignalclassificationbasedonspectrumshape AT ruilin lowcomplexityradarsignalclassificationbasedonspectrumshape AT xiaoleiwang lowcomplexityradarsignalclassificationbasedonspectrumshape AT yuliangyao lowcomplexityradarsignalclassificationbasedonspectrumshape AT linzhou lowcomplexityradarsignalclassificationbasedonspectrumshape AT yuanhe lowcomplexityradarsignalclassificationbasedonspectrumshape |