Adaptive radar detection and estimation schemes are often based on the independence of the secondary 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. Latest results from Random Matrix theory, used for large dimensional regime, allows to build a Toeplitz estimate of the spatial covariance matrix while the temporal covariance matrix is then estimated in a conventional way (Sample Covariance Matrix, M-estimates). These two joint estimates of the spatial and temporal covariance matrices leads to build Adaptive Radar Detectors, like Adaptive Normalized Matched Filter (ANMF). We show that taking care of the spatial covariance matrix may lead to significant performance improvements compared to classical procedures.