Adaptive censored greatest-of CFAR detection

A new CFAR detection algorithm in which two tentative estimates of the noise level in the test cell are obtained by independently processing the outputs of the leading and the lagging cells is proposed and studied. The two tentative estimates are obtained by employing a cell-by-cell criterion for accepting or rejecting reference samples. The final estimate of the noise level in the cell under test is set to be the maximum of the two tentative estimates. The proposed detection scheme is referred to as the `adaptive censored greatest-of´ (ACGO) CFAR detector. The false alarm regulation properties and the detection performance of the ACGO-CFAR processor are evaluated and compared with those of the `greatest-of´ (GO) CFAR and the `trimmed-mean´ (TM) CFAR detectors for both homogeneous and nonhomogeneous background environments