Spatial Parameter Identification for MIMO Systems in the Presence of Non-Gaussian Interference
Reliable identification of spatial parameters for multiple-input multiple-output (MIMO) systems, such as the number of transmit antennas (NTA) and the direction of arrival (DOA), is a prerequisite for MIMO signal separation and detection. Most existing parameter estimation methods for MIMO systems o...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/16/7/1243 |
| _version_ | 1797212047424880640 |
|---|---|
| author | Junlin Zhang Zihui Shi Yunfei Chen Mingqian Liu |
| author_facet | Junlin Zhang Zihui Shi Yunfei Chen Mingqian Liu |
| author_sort | Junlin Zhang |
| collection | DOAJ |
| description | Reliable identification of spatial parameters for multiple-input multiple-output (MIMO) systems, such as the number of transmit antennas (NTA) and the direction of arrival (DOA), is a prerequisite for MIMO signal separation and detection. Most existing parameter estimation methods for MIMO systems only consider a single parameter in Gaussian noise. This paper develops a reliable identification scheme based on generalized multi-antenna time-frequency distribution (GMTFD) for MIMO systems with non-Gaussian interference and Gaussian noise. First, a new generalized correlation matrix is introduced to construct a generalized MTFD matrix. Then, the covariance matrix based on time-frequency distribution (CM-TF) is characterized by using the diagonal entries from the auto-source signal components and the non-diagonal entries from the cross-source signal components in the generalized MTFD matrix. Finally, by making use of the CM-TF, the Gerschgorin disk criterion is modified to estimate NTA, and the multiple signal classification (MUSIC) is exploited to estimate DOA for MIMO system. Simulation results indicate that the proposed scheme based on GMTFD has good robustness to non-Gaussian interference without prior information and that it can achieve high estimation accuracy and resolution at low and medium signal-to-noise ratios (SNRs). |
| first_indexed | 2024-04-24T10:36:10Z |
| format | Article |
| id | doaj.art-db788301e95f49549d85593eead3fe0a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-24T10:36:10Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-db788301e95f49549d85593eead3fe0a2024-04-12T13:25:44ZengMDPI AGRemote Sensing2072-42922024-03-01167124310.3390/rs16071243Spatial Parameter Identification for MIMO Systems in the Presence of Non-Gaussian InterferenceJunlin Zhang0Zihui Shi1Yunfei Chen2Mingqian Liu3State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, ChinaState Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, ChinaDepartment of Engineering, University of Durham, Durham DH1 3LE, UKState Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, ChinaReliable identification of spatial parameters for multiple-input multiple-output (MIMO) systems, such as the number of transmit antennas (NTA) and the direction of arrival (DOA), is a prerequisite for MIMO signal separation and detection. Most existing parameter estimation methods for MIMO systems only consider a single parameter in Gaussian noise. This paper develops a reliable identification scheme based on generalized multi-antenna time-frequency distribution (GMTFD) for MIMO systems with non-Gaussian interference and Gaussian noise. First, a new generalized correlation matrix is introduced to construct a generalized MTFD matrix. Then, the covariance matrix based on time-frequency distribution (CM-TF) is characterized by using the diagonal entries from the auto-source signal components and the non-diagonal entries from the cross-source signal components in the generalized MTFD matrix. Finally, by making use of the CM-TF, the Gerschgorin disk criterion is modified to estimate NTA, and the multiple signal classification (MUSIC) is exploited to estimate DOA for MIMO system. Simulation results indicate that the proposed scheme based on GMTFD has good robustness to non-Gaussian interference without prior information and that it can achieve high estimation accuracy and resolution at low and medium signal-to-noise ratios (SNRs).https://www.mdpi.com/2072-4292/16/7/1243direction of arrivalgeneralized multi-antenna time-frequency distributionmultiple-input multiple-output systemsnumber of transmit antennas |
| spellingShingle | Junlin Zhang Zihui Shi Yunfei Chen Mingqian Liu Spatial Parameter Identification for MIMO Systems in the Presence of Non-Gaussian Interference Remote Sensing direction of arrival generalized multi-antenna time-frequency distribution multiple-input multiple-output systems number of transmit antennas |
| title | Spatial Parameter Identification for MIMO Systems in the Presence of Non-Gaussian Interference |
| title_full | Spatial Parameter Identification for MIMO Systems in the Presence of Non-Gaussian Interference |
| title_fullStr | Spatial Parameter Identification for MIMO Systems in the Presence of Non-Gaussian Interference |
| title_full_unstemmed | Spatial Parameter Identification for MIMO Systems in the Presence of Non-Gaussian Interference |
| title_short | Spatial Parameter Identification for MIMO Systems in the Presence of Non-Gaussian Interference |
| title_sort | spatial parameter identification for mimo systems in the presence of non gaussian interference |
| topic | direction of arrival generalized multi-antenna time-frequency distribution multiple-input multiple-output systems number of transmit antennas |
| url | https://www.mdpi.com/2072-4292/16/7/1243 |
| work_keys_str_mv | AT junlinzhang spatialparameteridentificationformimosystemsinthepresenceofnongaussianinterference AT zihuishi spatialparameteridentificationformimosystemsinthepresenceofnongaussianinterference AT yunfeichen spatialparameteridentificationformimosystemsinthepresenceofnongaussianinterference AT mingqianliu spatialparameteridentificationformimosystemsinthepresenceofnongaussianinterference |