Low-Complexity Bayesian-GLRT-Based Target Detection for MIMO Radar in Gaussian Clutter
This paper proposes an unstructured adaptive moving target detection approach for a multiple-input multiple-output (MIMO) radar to obtain reduced computational complexity. Moreover, the MIMO radar operates in a Gaussian clutter with an unknown but stochastic covariance matrix. At the design stage, t...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10444106/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1839617252565975040 |
|---|---|
| author | Yingtai Li Lisheng Yang Haowei Wu |
| author_facet | Yingtai Li Lisheng Yang Haowei Wu |
| author_sort | Yingtai Li |
| collection | DOAJ |
| description | This paper proposes an unstructured adaptive moving target detection approach for a multiple-input multiple-output (MIMO) radar to obtain reduced computational complexity. Moreover, the MIMO radar operates in a Gaussian clutter with an unknown but stochastic covariance matrix. At the design stage, the Bayesian unstructured generalized likelihood ratio test (UGLRT), namely the BUGLRT detector, which inherits from the UGLRT approach, has been derived. The BUGLRT approach has transformed 2D searching of structured generalized likelihood ratio test (SGLRT) based on the Bayesian framework, namely BSGLRT, detector into a 1D searching procedure that drastically reduces the computational complexity. The asymptotic probability of false alarms and computation complexity have also been analyzed. Both theoretical analysis and numerical simulation results show that BSGLRT and BUGLRT detectors outperform their non-Bayesian counterparts, especially for a small number of snapshots. Moreover, the detection performance of BUGLRT is better than that of BSGLRT, but the performance gap decreases as the number of snapshots increases. In addition, the detectors based on the Bayesian framework are sensitive to the existing mismatches of parameters for inverse complex Wishart distribution. Compared with BSGLRT, the BUGLRT can obtain a noticeable complexity reduction along with a slightly improved detection performance. |
| format | Article |
| id | doaj-art-c5105c7e20cc49a1bf5113efdeef0e3d |
| institution | Matheson Library |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c5105c7e20cc49a1bf5113efdeef0e3d2025-07-24T23:02:52ZengIEEEIEEE Access2169-35362025-01-011312572312573110.1109/ACCESS.2024.336906110444106Low-Complexity Bayesian-GLRT-Based Target Detection for MIMO Radar in Gaussian ClutterYingtai Li0https://orcid.org/0009-0002-5145-2245Lisheng Yang1Haowei Wu2https://orcid.org/0000-0001-5648-8955School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, ChinaSchool of Microelectronics and Communication Engineering, Chongqing University, Chongqing, ChinaSchool of Microelectronics and Communication Engineering, Chongqing University, Chongqing, ChinaThis paper proposes an unstructured adaptive moving target detection approach for a multiple-input multiple-output (MIMO) radar to obtain reduced computational complexity. Moreover, the MIMO radar operates in a Gaussian clutter with an unknown but stochastic covariance matrix. At the design stage, the Bayesian unstructured generalized likelihood ratio test (UGLRT), namely the BUGLRT detector, which inherits from the UGLRT approach, has been derived. The BUGLRT approach has transformed 2D searching of structured generalized likelihood ratio test (SGLRT) based on the Bayesian framework, namely BSGLRT, detector into a 1D searching procedure that drastically reduces the computational complexity. The asymptotic probability of false alarms and computation complexity have also been analyzed. Both theoretical analysis and numerical simulation results show that BSGLRT and BUGLRT detectors outperform their non-Bayesian counterparts, especially for a small number of snapshots. Moreover, the detection performance of BUGLRT is better than that of BSGLRT, but the performance gap decreases as the number of snapshots increases. In addition, the detectors based on the Bayesian framework are sensitive to the existing mismatches of parameters for inverse complex Wishart distribution. Compared with BSGLRT, the BUGLRT can obtain a noticeable complexity reduction along with a slightly improved detection performance.https://ieeexplore.ieee.org/document/10444106/Bayesian detectiongeneralized likelihood ratio test (GLRT)mutiple-input mutiple-output (MIMO) radarmaximum a posteriori (MAP)moving target detectionunstructured |
| spellingShingle | Yingtai Li Lisheng Yang Haowei Wu Low-Complexity Bayesian-GLRT-Based Target Detection for MIMO Radar in Gaussian Clutter IEEE Access Bayesian detection generalized likelihood ratio test (GLRT) mutiple-input mutiple-output (MIMO) radar maximum a posteriori (MAP) moving target detection unstructured |
| title | Low-Complexity Bayesian-GLRT-Based Target Detection for MIMO Radar in Gaussian Clutter |
| title_full | Low-Complexity Bayesian-GLRT-Based Target Detection for MIMO Radar in Gaussian Clutter |
| title_fullStr | Low-Complexity Bayesian-GLRT-Based Target Detection for MIMO Radar in Gaussian Clutter |
| title_full_unstemmed | Low-Complexity Bayesian-GLRT-Based Target Detection for MIMO Radar in Gaussian Clutter |
| title_short | Low-Complexity Bayesian-GLRT-Based Target Detection for MIMO Radar in Gaussian Clutter |
| title_sort | low complexity bayesian glrt based target detection for mimo radar in gaussian clutter |
| topic | Bayesian detection generalized likelihood ratio test (GLRT) mutiple-input mutiple-output (MIMO) radar maximum a posteriori (MAP) moving target detection unstructured |
| url | https://ieeexplore.ieee.org/document/10444106/ |
| work_keys_str_mv | AT yingtaili lowcomplexitybayesianglrtbasedtargetdetectionformimoradaringaussianclutter AT lishengyang lowcomplexitybayesianglrtbasedtargetdetectionformimoradaringaussianclutter AT haoweiwu lowcomplexitybayesianglrtbasedtargetdetectionformimoradaringaussianclutter |