Adaptive subspace detection of range-distributed target in compound-Gaussian clutter
Digital Signal Processing
CFAR detection strategies for distributed targets under conic constraints
IEEE Transactions on Signal Processing
Distributed target detection with polarimetric MIMO radar in compound-Gaussian clutter
Digital Signal Processing
GLRT-based adaptive detection algorithms for range-spread targets
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
The CFAR adaptive subspace detector is a scale-invariant GLRT
IEEE Transactions on Signal Processing
GLRT-Based Direction Detectors in Homogeneous Noise and Subspace Interference
IEEE Transactions on Signal Processing
Adaptive detection of distributed targets in compound-Gaussian clutter with inverse gamma texture
Digital Signal Processing
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In this paper we deal with the problem of detecting distributed targets in the presence of Gaussian noise with unknown but persymmetric structured covariance matrix. In particular, we consider the so-called partially-homogeneous environment, where the cells under test (primary data) and the training samples (secondary data), which are free of signal components, share the same structure of the interference covariance matrix but different power levels. Under the above assumptions, we derive the generalized likelihood ratio test (GLRT) and the so-called two-step GLRT. Remarkably, the new receivers ensure the constant false alarm rate property with respect to both the structure of the covariance matrix as well as the power level. The performance assessment, conducted by resorting to both simulated data and real recorded dataset, highlights that the proposed detectors can significantly outperform their unstructured counterparts, especially in a severely heterogeneous scenario where a very small number of secondary data is available.