@InProceedings{8081373,
  author = {W. {Ben Abdallah} and J. P. Ovarlez and P. Bondon},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Radar detection schemes for joint temporal and spatial correlated clutter using vector ARMA models},
  year = {2017},
  pages = {1075-1079},
  abstract = {Adaptive radar detection and estimation schemes are often based on the independence of the training data used for building estimators and detectors. This paper relaxes this constraint and deals with the non-trivial problem of deriving detection and estimation schemes for joint spatial and temporal correlated radar measurements. In order to estimate these two joint correlation matrices, we propose to use the Vector ARMA (VARMA) methodology. The estimation of the VARMA model parameters are performed with Maximum Likelihood Estimators in Gaussian and non-Gaussian environment. These two joint estimates of the spatial and temporal covariance matrices leads to build Adaptive Radar Detectors, like Adaptive Normalized Matched Filter (ANMF). Their corresponding performance are analyzed through simulated datasets. We show that taking into account the spatial covariance matrix may lead to significant performance improvements compared to classical procedures ignoring the spatial correlation.},
  keywords = {adaptive filters;adaptive radar;covariance matrices;maximum likelihood estimation;radar clutter;radar detection;training data;building estimators;nontrivial problem;temporal correlated radar measurements;joint correlation matrices;Vector ARMA methodology;VARMA model parameters;Maximum Likelihood Estimators;spatial covariance matrices;temporal covariance matrices;Adaptive Radar Detectors;Adaptive Normalized Matched Filter;spatial correlation;Radar detection schemes;joint temporal clutter;spatial correlated clutter;vector ARMA models;Adaptive radar detection;spatial covariance matrix;Clutter;Covariance matrices;Radar detection;Correlation;Maximum likelihood estimation;Detectors},
  doi = {10.23919/EUSIPCO.2017.8081373},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347210.pdf},
}

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This paper relaxes this constraint and deals with the non-trivial problem of deriving detection and estimation schemes for joint spatial and temporal correlated radar measurements. In order to estimate these two joint correlation matrices, we propose to use the Vector ARMA (VARMA) methodology. The estimation of the VARMA model parameters are performed with Maximum Likelihood Estimators in Gaussian and non-Gaussian environment. These two joint estimates of the spatial and temporal covariance matrices leads to build Adaptive Radar Detectors, like Adaptive Normalized Matched Filter (ANMF). Their corresponding performance are analyzed through simulated datasets. 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P. Ovarlez and P. Bondon},\n booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},\n title = {Radar detection schemes for joint temporal and spatial correlated clutter using vector ARMA models},\n year = {2017},\n pages = {1075-1079},\n abstract = {Adaptive radar detection and estimation schemes are often based on the independence of the training data used for building estimators and detectors. This paper relaxes this constraint and deals with the non-trivial problem of deriving detection and estimation schemes for joint spatial and temporal correlated radar measurements. In order to estimate these two joint correlation matrices, we propose to use the Vector ARMA (VARMA) methodology. The estimation of the VARMA model parameters are performed with Maximum Likelihood Estimators in Gaussian and non-Gaussian environment. These two joint estimates of the spatial and temporal covariance matrices leads to build Adaptive Radar Detectors, like Adaptive Normalized Matched Filter (ANMF). Their corresponding performance are analyzed through simulated datasets. 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