Resolution enhancement of active sonar can suppress the reverberation.While it also makes the envelope data distribution diverge from Rayleigh distribution to K-distribution.The stronger scattering speckles,the heavier of the K-distribution tails.The envelope amplitudes of these strong scattering speckles are usually very big.As the interfering target,the strong reverberation decreases the performances of the background power level estimation and the target detection.The fuzzy statistical normalization processing(FSNP) is introduced to suppress the strong reverberation firstly in this paper.Then how the strong reverberation and the FSNP affect the distribution of K-distributed sonar data is studied.The influence on the constant false alarm rate(CFAR) detection performance caused by the strong reverberation and the FSNP is also simulated and analyzed.Performance comparisons between the CFAR detector based on FSNP and the conventional CFAR detectors are carried out.The simulation results show that the strong reverberation can make the shape parameter of the interfering K-distributed data become smaller than that of the original K-distributed data.While the FSNP can suppress the strong reverberation,increase the shape parameter value,and improve the performance of the shape parameter estimator and the CFAR detector. Resolution enhancement of active sonar can suppress the reverberation. Whi it also makes the envelope data distribution diverge from Rayleigh distribution to K-distribution. The strong scattering speckles, the heavier of the K-distribution tails. Envelope amplitudes of these strong scattering speckles are usually very big. As the interfering target, the strong reverberation decreases the performances of the background power level estimation and the target detection.The fuzzy statistical normalization processing (FSNP) is introduced to suppress the strong reverberation first in this paper. reverberation and the FSNP affect the distribution of K-distributed sonar data is studied. The influence on the constant false alarm rate (CFAR) detection performance caused by the strong reverberation and the FSNP is also simulated and analyzed. Performance comparisons between the CFAR detector based on FSNP and the conventional CFAR detectors are carried out. The simulation results show that the strong reverberation can make the shape parameter of the interfering K-distributed data become smaller than that of the original K-distributed data. Whilst the FSNP can suppress the strong reverberation, increase the shape parameter value, and improve the performance of the shape parameter estimator and the CFAR detector.