Automatic censoring CFAR detector based on ordered data variability for nonhomogeneous environments

The authors propose an automatic censored cell averaging (ACCA) CFAR detector based on ordered data variability (ODV) for nonhomogeneous background environments. The ACCA-ODV detector selects dynamically, by doing successive hypothesis tests, a suitable set of ranked cells to estimate the unknown background level. The proposed detector does not require any prior information about the background environment and uses the variability index statistic as a shape parameter to reject or accept the ordered cells under investigation. For implementation purposes, the authors suggest a two-level architecture in which both the successive ODV-based statistics and the corresponding hypothesis tests are processed simultaneously. The performance of the proposed detector is evaluated and compared with those of the OS-CFAR and the variability index-CFAR (VI-CFAR) detectors in various background environments. The results show that the ACCA-ODV detector acts like the CA-CFAR in a homogeneous background and performs robustly in nonhomogeneous environments