Abstract
In practice, there are two common situations when the independent and identically distributed (IID) assumption no longer holds: (i) there is a clutter edge and (ii) there is an outlier, e.g., a clutter spike, an impulsive interference, or another interfering target. These can result in masking of weaker targets near stronger ones and excessive false alarms at clutter edge transitions. In this paper, a new constant false alarm (CFAR) detector is proposed, which uses a goodness of fit test to verify the IID assumption. If it is decided that the data in the reference window is IID, the cell averaging (CA)-detector is applied. Otherwise, a range-heterogeneous detection algorithm is applied to provide homogeneous samples to develop a CA-based detector. The performance study shows that the proposed detector performs like the CA detector in the homogeneous situation and outperforms other competing CFAR detectors in heterogeneous situations caused by multiple targets and clutter edge.
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