A new polarimetric subspace target detector based on the dihedral signal model for bight peaks within a spatially extended SAR target signature has previously been developed. This new polarimetric subspace target detector uses a very general spherically invariant random vector (SIRV) model with additive clutter for the SAR data. The SIRV model includes both the Gaussian and K-distributions which are commonly used to model SAR data. In this paper, we present performance results for several versions of this new target detector against real and simulated SAR data. We show that the Gaussian dihedral detector does a better job of separating a set of tactical military targets from natural clutter compared to detectors that assume no knowledge about the polarimetric structure of the target signal including the PWF detector. We show that the K-distribution version of the dihedral detector performs very poorly against real SAR data. The GLRT used to develop the dihedral detector has no guarantee of optimality. We explore the accuracy of our signal model, and the accuracy and robustness of our parameter estimators to reveal the limitations of the GLRT approach for non-Gaussian approaches to this problem.
Victor J. Larson,
"Performance of K-distribution GLRT SAR target detector", Proc. SPIE 2755, Signal Processing, Sensor Fusion, and Target Recognition V, (14 June 1996); doi: 10.1117/12.243188; https://doi.org/10.1117/12.243188