Passive MIMO Radar Detection with Unknown Colored Gaussian Noise
The target detection of the passive multiple-input multiple-output (MIMO) radar that is comprised of multiple illuminators of opportunity and multiple receivers is investigated in this paper. In the passive MIMO radar, the transmitted signals of illuminators of opportunity are totally unknown, and t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/14/2708 |
| _version_ | 1797526133555593216 |
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| author | Yongjun Liu Guisheng Liao Haichuan Li Shengqi Zhu Yachao Li Yingzeng Yin |
| author_facet | Yongjun Liu Guisheng Liao Haichuan Li Shengqi Zhu Yachao Li Yingzeng Yin |
| author_sort | Yongjun Liu |
| collection | DOAJ |
| description | The target detection of the passive multiple-input multiple-output (MIMO) radar that is comprised of multiple illuminators of opportunity and multiple receivers is investigated in this paper. In the passive MIMO radar, the transmitted signals of illuminators of opportunity are totally unknown, and the received signals are contaminated by the colored Gaussian noise with an unknown covariance matrix. The generalized likelihood ratio test (GLRT) is explored for the passive MIMO radar when the channel coefficients are also unknown, and the closed-form GLRT is derived. Compared with the GLRT with unknown transmitted signals and channel coefficients but a known covariance matrix, the proposed method is applicable for a more practical case whenthe covariance matrix of colored noise is unknown, although it has higher computational complexity. Moreover, the proposed GLRT can achieve similar performance as the GLRT with the known covariance matrix when the number of training samples is large enough. Finally, the effectiveness of the proposed GLRT is verified by several numerical examples. |
| first_indexed | 2024-03-10T09:25:52Z |
| format | Article |
| id | doaj.art-9f23b1374a5c4cd0811f92a54d563408 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T09:25:52Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-9f23b1374a5c4cd0811f92a54d5634082023-11-22T04:51:13ZengMDPI AGRemote Sensing2072-42922021-07-011314270810.3390/rs13142708Passive MIMO Radar Detection with Unknown Colored Gaussian NoiseYongjun Liu0Guisheng Liao1Haichuan Li2Shengqi Zhu3Yachao Li4Yingzeng Yin5National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Key Laboratory of Antennas and Microwave Technology, Xidian University, Xi’an 710071, ChinaThe target detection of the passive multiple-input multiple-output (MIMO) radar that is comprised of multiple illuminators of opportunity and multiple receivers is investigated in this paper. In the passive MIMO radar, the transmitted signals of illuminators of opportunity are totally unknown, and the received signals are contaminated by the colored Gaussian noise with an unknown covariance matrix. The generalized likelihood ratio test (GLRT) is explored for the passive MIMO radar when the channel coefficients are also unknown, and the closed-form GLRT is derived. Compared with the GLRT with unknown transmitted signals and channel coefficients but a known covariance matrix, the proposed method is applicable for a more practical case whenthe covariance matrix of colored noise is unknown, although it has higher computational complexity. Moreover, the proposed GLRT can achieve similar performance as the GLRT with the known covariance matrix when the number of training samples is large enough. Finally, the effectiveness of the proposed GLRT is verified by several numerical examples.https://www.mdpi.com/2072-4292/13/14/2708radar detectionpassive radarcolored Gaussian noisegeneralized likelihood ratio testmultiple-input multiple-output |
| spellingShingle | Yongjun Liu Guisheng Liao Haichuan Li Shengqi Zhu Yachao Li Yingzeng Yin Passive MIMO Radar Detection with Unknown Colored Gaussian Noise Remote Sensing radar detection passive radar colored Gaussian noise generalized likelihood ratio test multiple-input multiple-output |
| title | Passive MIMO Radar Detection with Unknown Colored Gaussian Noise |
| title_full | Passive MIMO Radar Detection with Unknown Colored Gaussian Noise |
| title_fullStr | Passive MIMO Radar Detection with Unknown Colored Gaussian Noise |
| title_full_unstemmed | Passive MIMO Radar Detection with Unknown Colored Gaussian Noise |
| title_short | Passive MIMO Radar Detection with Unknown Colored Gaussian Noise |
| title_sort | passive mimo radar detection with unknown colored gaussian noise |
| topic | radar detection passive radar colored Gaussian noise generalized likelihood ratio test multiple-input multiple-output |
| url | https://www.mdpi.com/2072-4292/13/14/2708 |
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