高分辨率有源声呐强混响抑制技术研究

有源声呐分辨率的提高可以抑制混响,但也使声呐包络数据的统计分布偏离瑞利分布,而更接近拖尾较重的K分布。强散射体所带来的强混响的幅值一般较大,它们使统计分布拖尾更严重,表征K分布的形状参数也越小。强混响作为目标干扰,严重影响了背景功率估计的准确性,从而降低了目标检测的性能。本文基于模糊统计理论,首先提出了用于抑制强混响的模糊统计归一化处理方法;然后对强混响和模糊统计归一化处理如何影响声呐数据分布和CFAR(Constant false-alarm rate)目标检测性能进行了仿真、研究和分析,最后对基于模糊统计归一化处理的CFAR检测性能和传统CFAR检测性能进行了仿真比较。仿真结果表明强混响目标干扰能使K分布数据的形状参数变小,而模糊统计归一化处理可抑制强混响目标干扰,增大包络数据分布的形状参数,提高形状参数估计性能和CFAR检测性能。 

Research on strong reverberation suppression for high resolution active sonar

Resolution enhancement of active sonar can suppress the reverberation. While it also makes the envelop data distribution diverge from Rayleigh 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.