Innovative CFAR detector with effective parameter estimation method for generalised gamma distribution and iterative sliding window strategy

Abstract

For high-resolution synthetic aperture radar (SAR), constant false alarm rate (CFAR) detectors are widely used to separate targets from background and it is proved that generalised gamma distribution (GΓD) can model the non-homogeneous clutter appropriately. However, CFAR detectors based on GΓD possess two problems. First, the methods for solving highly nonlinear equations of GΓD resort to numerical iterative algorithms, which are computationally expensive. To avoid this, the authors present a novel analytic solution for parameter estimation of GΓD by exploiting a third-order approximation of the polygamma function. This novel analytic solution can result in more accurate parameter estimation and can fit a wider range of the parameters. The second problem is that in a multi-target SAR image, some target pixels may be classified as clutter pixels. To select pixels of interest, an iterative sliding window approach is often used in CFAR. They analyse the relationship between the detection probability and the number of iterations, and prove that this strategy can reduce the miss rate and false detection rate effectively. On the basis of the aforementioned parameter estimation method and the iterative sliding window approach, an innovative and effective CFAR detector is proposed in this study and its superiority is demonstrated by experiments.