A Time-Frequency Domain Underdetermined Blind Source Separation Algorithm for MIMO Radar Signals
This paper considers the underdetermined blind separation of multiple input multiple output (MIMO) radar signals that are insufficiently sparse in both time and frequency domains under noisy conditions, while traditional algorithms are usually applied in the ideal sparse environment. An effective se...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-07-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/9/7/104 |
| _version_ | 1811186108971614208 |
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| author | Qiang Guo Guoqing Ruan Yanping Liao |
| author_facet | Qiang Guo Guoqing Ruan Yanping Liao |
| author_sort | Qiang Guo |
| collection | DOAJ |
| description | This paper considers the underdetermined blind separation of multiple input multiple output (MIMO) radar signals that are insufficiently sparse in both time and frequency domains under noisy conditions, while traditional algorithms are usually applied in the ideal sparse environment. An effective separation method based on single source point (SSP) identification and time-frequency smoothed l 0 norm (TF-SL0) is proposed. Firstly, a preprocessing step of the moving average filter and a novel argument-based time-frequency SSPs detection are employed to improve the signal-to-noise ratio and signal sparsity of the observed signals, respectively. Then, the mixing matrix is obtained by using clustering algorithms. Secondly, to obtain the optimal solution of underdetermined sparse component analysis, the smoothed l 0 norm (SL0) is introduced to preliminarily achieve signal separation in the time-frequency domain. Finally, time-frequency ridge estimation is proposed to jointly enhance the reconstruction accuracy of the MIMO radar signals, and the time domain waveforms are recovered by the model of the signals. Simulations illustrate the validity of the method and show that the proposed method outperforms the traditional methods in source separation, especially in the non-cooperative electromagnetic case where the prior information is unknown. |
| first_indexed | 2024-04-11T13:41:09Z |
| format | Article |
| id | doaj.art-c649282717364cdfb2160c4743c83118 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-11T13:41:09Z |
| publishDate | 2017-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-c649282717364cdfb2160c4743c831182022-12-22T04:21:16ZengMDPI AGSymmetry2073-89942017-07-019710410.3390/sym9070104sym9070104A Time-Frequency Domain Underdetermined Blind Source Separation Algorithm for MIMO Radar SignalsQiang Guo0Guoqing Ruan1Yanping Liao2College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaThis paper considers the underdetermined blind separation of multiple input multiple output (MIMO) radar signals that are insufficiently sparse in both time and frequency domains under noisy conditions, while traditional algorithms are usually applied in the ideal sparse environment. An effective separation method based on single source point (SSP) identification and time-frequency smoothed l 0 norm (TF-SL0) is proposed. Firstly, a preprocessing step of the moving average filter and a novel argument-based time-frequency SSPs detection are employed to improve the signal-to-noise ratio and signal sparsity of the observed signals, respectively. Then, the mixing matrix is obtained by using clustering algorithms. Secondly, to obtain the optimal solution of underdetermined sparse component analysis, the smoothed l 0 norm (SL0) is introduced to preliminarily achieve signal separation in the time-frequency domain. Finally, time-frequency ridge estimation is proposed to jointly enhance the reconstruction accuracy of the MIMO radar signals, and the time domain waveforms are recovered by the model of the signals. Simulations illustrate the validity of the method and show that the proposed method outperforms the traditional methods in source separation, especially in the non-cooperative electromagnetic case where the prior information is unknown.https://www.mdpi.com/2073-8994/9/7/104blind source separationunderdetermined mixturesMIMO radar signalssingle source points identificationsparse recovery |
| spellingShingle | Qiang Guo Guoqing Ruan Yanping Liao A Time-Frequency Domain Underdetermined Blind Source Separation Algorithm for MIMO Radar Signals Symmetry blind source separation underdetermined mixtures MIMO radar signals single source points identification sparse recovery |
| title | A Time-Frequency Domain Underdetermined Blind Source Separation Algorithm for MIMO Radar Signals |
| title_full | A Time-Frequency Domain Underdetermined Blind Source Separation Algorithm for MIMO Radar Signals |
| title_fullStr | A Time-Frequency Domain Underdetermined Blind Source Separation Algorithm for MIMO Radar Signals |
| title_full_unstemmed | A Time-Frequency Domain Underdetermined Blind Source Separation Algorithm for MIMO Radar Signals |
| title_short | A Time-Frequency Domain Underdetermined Blind Source Separation Algorithm for MIMO Radar Signals |
| title_sort | time frequency domain underdetermined blind source separation algorithm for mimo radar signals |
| topic | blind source separation underdetermined mixtures MIMO radar signals single source points identification sparse recovery |
| url | https://www.mdpi.com/2073-8994/9/7/104 |
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