A CFAR detector using a data discrimination algorithm

In a real radar environment, the presence of clutter power transitions and/or interfering target returns in the range resolution cells of a constant false alarm rate (CFAR) detector may cause an excessive number of false alarms or impaired target detectability. To obtain a robust estimate of the noise level in the test cell, the trimmed mean (TM) and the adaptive censored greatest-of (ACGO) CFAR detectors have been proposed in the literature. In this paper, we propose a data processing algorithm which performs two passes over the data. In the first pass, the objective of the algorithm is to censor possible interfering target returns that may be present in the reference cells of the test cell. In the second pass the algorithm determines whether the test cell is in the clutter or the clear region and selects only those samples which are identically distributed with the noise in the test cell to form the detection threshold. The proposed detector is referred to as the data discrimination (DD) CFAR processor. Unlike the TM and the ACGO CFAR processors, the DD-CFAR detector does not require rank ordering of the received data