Matrix Information Geometry for Spectral-Based SPD Matrix Signal Detection with Dimensionality Reduction
In this paper, a novel signal detector based on matrix information geometric dimensionality reduction (DR) is proposed, which is inspired from spectrogram processing. By short time Fourier transform (STFT), the received data are represented as a 2-D high-precision spectrogram, from which we can well...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/9/914 |
| _version_ | 1797556639803375616 |
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| author | Sheng Feng Xiaoqiang Hua Xiaoqian Zhu |
| author_facet | Sheng Feng Xiaoqiang Hua Xiaoqian Zhu |
| author_sort | Sheng Feng |
| collection | DOAJ |
| description | In this paper, a novel signal detector based on matrix information geometric dimensionality reduction (DR) is proposed, which is inspired from spectrogram processing. By short time Fourier transform (STFT), the received data are represented as a 2-D high-precision spectrogram, from which we can well judge whether the signal exists. Previous similar studies extracted insufficient information from these spectrograms, resulting in unsatisfactory detection performance especially for complex signal detection task at low signal-noise-ratio (SNR). To this end, we use a global descriptor to extract abundant features, then exploit the advantages of matrix information geometry technique by constructing the high-dimensional features as symmetric positive definite (SPD) matrices. In this case, our task for signal detection becomes a binary classification problem lying on an SPD manifold. Promoting the discrimination of heterogeneous samples through information geometric DR technique that is dedicated to SPD manifold, our proposed detector achieves satisfactory signal detection performance in low SNR cases using the K distribution simulation and the real-life sea clutter data, which can be widely used in the field of signal detection. |
| first_indexed | 2024-03-10T17:04:41Z |
| format | Article |
| id | doaj.art-3a85cb90ce0047ee814a9cc74c10a9e1 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T17:04:41Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-3a85cb90ce0047ee814a9cc74c10a9e12023-11-20T10:50:09ZengMDPI AGEntropy1099-43002020-08-0122991410.3390/e22090914Matrix Information Geometry for Spectral-Based SPD Matrix Signal Detection with Dimensionality ReductionSheng Feng0Xiaoqiang Hua1Xiaoqian Zhu2College of Computer Science, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaIn this paper, a novel signal detector based on matrix information geometric dimensionality reduction (DR) is proposed, which is inspired from spectrogram processing. By short time Fourier transform (STFT), the received data are represented as a 2-D high-precision spectrogram, from which we can well judge whether the signal exists. Previous similar studies extracted insufficient information from these spectrograms, resulting in unsatisfactory detection performance especially for complex signal detection task at low signal-noise-ratio (SNR). To this end, we use a global descriptor to extract abundant features, then exploit the advantages of matrix information geometry technique by constructing the high-dimensional features as symmetric positive definite (SPD) matrices. In this case, our task for signal detection becomes a binary classification problem lying on an SPD manifold. Promoting the discrimination of heterogeneous samples through information geometric DR technique that is dedicated to SPD manifold, our proposed detector achieves satisfactory signal detection performance in low SNR cases using the K distribution simulation and the real-life sea clutter data, which can be widely used in the field of signal detection.https://www.mdpi.com/1099-4300/22/9/914dimensionality reductionsignal detectionSPD manifoldspectrogram processing |
| spellingShingle | Sheng Feng Xiaoqiang Hua Xiaoqian Zhu Matrix Information Geometry for Spectral-Based SPD Matrix Signal Detection with Dimensionality Reduction Entropy dimensionality reduction signal detection SPD manifold spectrogram processing |
| title | Matrix Information Geometry for Spectral-Based SPD Matrix Signal Detection with Dimensionality Reduction |
| title_full | Matrix Information Geometry for Spectral-Based SPD Matrix Signal Detection with Dimensionality Reduction |
| title_fullStr | Matrix Information Geometry for Spectral-Based SPD Matrix Signal Detection with Dimensionality Reduction |
| title_full_unstemmed | Matrix Information Geometry for Spectral-Based SPD Matrix Signal Detection with Dimensionality Reduction |
| title_short | Matrix Information Geometry for Spectral-Based SPD Matrix Signal Detection with Dimensionality Reduction |
| title_sort | matrix information geometry for spectral based spd matrix signal detection with dimensionality reduction |
| topic | dimensionality reduction signal detection SPD manifold spectrogram processing |
| url | https://www.mdpi.com/1099-4300/22/9/914 |
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