A polarimetric adaptive matched filter
Signal Processing
Covariance matrix estimation for CFAR Detection in correlated heavy tailed clutter
Signal Processing - Signal processing with heavy-tailed models
Polarimetric adaptive detection in non-Gaussian noise
Signal Processing
Polarimetric MIMO radar with distributed antennas for target detection
IEEE Transactions on Signal Processing
Polarimetric adaptive detection of range-distributed targets
IEEE Transactions on Signal Processing
GLRT-based adaptive detection algorithms for range-spread targets
IEEE Transactions on Signal Processing
On Probing Signal Design For MIMO Radar
IEEE Transactions on Signal Processing
Spatial diversity in radars-models and detection performance
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Performance Analysis of Covariance Matrix Estimates in Impulsive Noise
IEEE Transactions on Signal Processing
Iterative Generalized-Likelihood Ratio Test for MIMO Radar
IEEE Transactions on Signal Processing
Adaptive detection of distributed targets in compound-Gaussian clutter with inverse gamma texture
Digital Signal Processing
Radar detection algorithm for GARCH clutter model
Digital Signal Processing
Persymmetric adaptive detection of distributed targets in partially-homogeneous environment
Digital Signal Processing
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This paper mainly deals with distributed targets detection with a polarimetric MIMO radar against compound-Gaussian clutter dominated scenario with unknown covariance matrix. First, the general polarimetric detecting problem of the distributed targets is developed to the MIMO radar, and then, the fully adaptive Generalized Likelihood Ratio Test (GLRT) is devised according to the well known two-step design procedure. Three covariance matrix estimation strategies using the secondary data are introduced to make derived receiver fully adaptive. A thorough performance assessment is given, and via several numerical examples, the results highlight that the spatial and polarization diversities can be exploited to improve the detection performance of the distributed targets in compound-Gaussian background, and it outperforms the phased-array counterpart, the adaptive loss is completely acceptable in practical applications. Meanwhile, the fixed-point estimation strategy is more suitable to implement the adaptive detection algorithm.