Novel Range-Spread Target Detectors in Non-Gaussian Clutter

Range-spread target detection in spherically invariant random vector clutter is addressed, and different detectors with constant false alarm rate (CFAR) property are devised by exploiting order statistics theory. Firstly, with a known normalized clutter covariance matrix, the generalized likelihood ratio test based on order statistics (OS-GLRT) utilizes some largest observations from the range cells occupied by the most likely target scatterers. OS-GLRT is robust when the estimated number of scatterers is somewhat larger than the actual, but is degraded for smaller estimations. To improve the robustness of OS-GLRT, an OS-GLRT with dynamic threshold (DOS-GLRT) is designed, which adjusts detection threshold dynamically. By replacing the ideal normalized clutter covariance matrix with the constrained approximate maximum likelihood (ML) estimated matrix based on secondary data only, the adaptive OS-GLRT and adaptive DOS-GLRT are also obtained. The performance assessment conducted by Monte Carlo simulation confirms the effectiveness of the proposed detectors.