One of the most important tasks in radar systems is target detection. Constant False Alarm Rate (CFAR) property is a good criterion for detector design and a detector with this property has some advantages. In these detectors, while the statistics of noise and clutter changes, the performance of detection doesn’t change. In order to achieve CFAR property in varying environments, the basic approach is using an adaptive threshold. Heretofore so many detectors and algorithms have been introduced to reach CFAR property in radar applications. In this research, we review some of these detectors and study some of their most important Then, we will have an overview on wavelet transform, statistical signal processing and wavelet-based denoising algorithms. After that, we propose two kinds of CFAR detectors using wavelet shrinkage. It is shown that the proposed detectors have good performance in homogeneous environments and have a very good robustness in nonhomogeneous situations without any priori knowledge requirements about environments. Performance of these detectors will be compared with some conventional CFAR processors and their good performance and robustness in nongaussian clutter is shown. These processors have some parameters which can be set properly according to environmental situations. Methods of setting these parameters have been developed and discussed accurately.
