Sparse Bayesian Learning-Based Target Imaging and Parameter Estimation for Monostatic MIMO Radar Systems
This paper presents novel sparse Bayesian learning (SBL)-based target imaging and parameter estimation techniques in monostatic multiple-input multiple-output (MIMO) radar systems for practical scenarios with insufficient observation samples and unknown target parameters. First, the SBL framework is...
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2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8529199/ |
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doaj-805ecac462854776a750b41ff5c6406b2021-03-29T21:37:30ZengIEEEIEEE Access2169-35362018-01-016685456855910.1109/ACCESS.2018.28802428529199Sparse Bayesian Learning-Based Target Imaging and Parameter Estimation for Monostatic MIMO Radar SystemsAmrita Mishra0https://orcid.org/0000-0003-0397-6781Vini Gupta1Saumya Dwivedi2Aditya K. Jagannatham3Pramod K. Varshney4Department of Electrical Engineering, IIT Kanpur, Kanpur, IndiaDepartment of Electrical Engineering, IIT Delhi, New Delhi, IndiaDepartment of Electrical Engineering, IIT Kanpur, Kanpur, IndiaDepartment of Electrical Engineering, IIT Kanpur, Kanpur, IndiaDepartment of Electrical Engineering & Computer Science, Syracuse University, Syracuse, NY, USAThis paper presents novel sparse Bayesian learning (SBL)-based target imaging and parameter estimation techniques in monostatic multiple-input multiple-output (MIMO) radar systems for practical scenarios with insufficient observation samples and unknown target parameters. First, the SBL framework is developed for a single measurement vector setting with an underlying sparse target reflectivity parameter vector. This is subsequently extended to scenarios with multiple observation snapshots considering uncorrelated as well as correlated target reflectivity parameters. Variants are also proposed for challenging scenarios considering the presence of ground clutter. Cramér-Rao bounds are derived for the reflectivity, Doppler, and range estimates to comprehensively characterize the performance of the proposed estimation schemes. A joint parameter estimation and imaging scheme is developed based on a Taylor series expansion of the MIMO radar dictionary matrix. Simulation results demonstrate enhanced imaging and estimation accuracy of the proposed SBL schemes in comparison with the existing techniques for MIMO radar systems.https://ieeexplore.ieee.org/document/8529199/Monostatic MIMO radarsparse Bayesian learningtarget imagingparameter estimationCramér-Rao bound |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Amrita Mishra Vini Gupta Saumya Dwivedi Aditya K. Jagannatham Pramod K. Varshney |
| spellingShingle |
Amrita Mishra Vini Gupta Saumya Dwivedi Aditya K. Jagannatham Pramod K. Varshney Sparse Bayesian Learning-Based Target Imaging and Parameter Estimation for Monostatic MIMO Radar Systems IEEE Access Monostatic MIMO radar sparse Bayesian learning target imaging parameter estimation Cramér-Rao bound |
| author_facet |
Amrita Mishra Vini Gupta Saumya Dwivedi Aditya K. Jagannatham Pramod K. Varshney |
| author_sort |
Amrita Mishra |
| title |
Sparse Bayesian Learning-Based Target Imaging and Parameter Estimation for Monostatic MIMO Radar Systems |
| title_short |
Sparse Bayesian Learning-Based Target Imaging and Parameter Estimation for Monostatic MIMO Radar Systems |
| title_full |
Sparse Bayesian Learning-Based Target Imaging and Parameter Estimation for Monostatic MIMO Radar Systems |
| title_fullStr |
Sparse Bayesian Learning-Based Target Imaging and Parameter Estimation for Monostatic MIMO Radar Systems |
| title_full_unstemmed |
Sparse Bayesian Learning-Based Target Imaging and Parameter Estimation for Monostatic MIMO Radar Systems |
| title_sort |
sparse bayesian learning-based target imaging and parameter estimation for monostatic mimo radar systems |
| publisher |
IEEE |
| series |
IEEE Access |
| issn |
2169-3536 |
| publishDate |
2018-01-01 |
| description |
This paper presents novel sparse Bayesian learning (SBL)-based target imaging and parameter estimation techniques in monostatic multiple-input multiple-output (MIMO) radar systems for practical scenarios with insufficient observation samples and unknown target parameters. First, the SBL framework is developed for a single measurement vector setting with an underlying sparse target reflectivity parameter vector. This is subsequently extended to scenarios with multiple observation snapshots considering uncorrelated as well as correlated target reflectivity parameters. Variants are also proposed for challenging scenarios considering the presence of ground clutter. Cramér-Rao bounds are derived for the reflectivity, Doppler, and range estimates to comprehensively characterize the performance of the proposed estimation schemes. A joint parameter estimation and imaging scheme is developed based on a Taylor series expansion of the MIMO radar dictionary matrix. Simulation results demonstrate enhanced imaging and estimation accuracy of the proposed SBL schemes in comparison with the existing techniques for MIMO radar systems. |
| topic |
Monostatic MIMO radar sparse Bayesian learning target imaging parameter estimation Cramér-Rao bound |
| url |
https://ieeexplore.ieee.org/document/8529199/ |
| work_keys_str_mv |
AT amritamishra sparsebayesianlearningbasedtargetimagingandparameterestimationformonostaticmimoradarsystems AT vinigupta sparsebayesianlearningbasedtargetimagingandparameterestimationformonostaticmimoradarsystems AT saumyadwivedi sparsebayesianlearningbasedtargetimagingandparameterestimationformonostaticmimoradarsystems AT adityakjagannatham sparsebayesianlearningbasedtargetimagingandparameterestimationformonostaticmimoradarsystems AT pramodkvarshney sparsebayesianlearningbasedtargetimagingandparameterestimationformonostaticmimoradarsystems |
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1724192523926110208 |