Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/CAMSAP.2015.7383758 | 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) |
Keywords | Field | DocType |
adaptive normalized matched filter,adaptive radar detector,temporal covariance matrices,spatial covariance matrices,M-estimates,sample covariance matrix,temporal covariance matrix,Toeplitz estimation,random matrix theory,signal estimation,radar detection,whitening space correlation,RMT | Covariance function,Estimation of covariance matrices,Rational quadratic covariance function,Covariance intersection,Algorithm,Covariance matrix,Statistics,Matérn covariance function,Covariance mapping,Mathematics,Covariance | Conference |
Citations | PageRank | References |
1 | 0.39 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Romain Couillet | 1 | 692 | 74.03 |
| M. Greco | 2 | 203 | 25.71 |
| Jean Philippe Ovarlez | 3 | 190 | 25.11 |
| Frédéric Pascal | 4 | 175 | 23.99 |