具有提升抗干扰能力的距离扩展目标模糊CFAR检测方法

A Fuzzy CFAR Detection Method for Range-extended Targets with Enhanced Anti-jamming Capability

为提高高分辨声纳在自动检测时对抗多目标干扰的能力,本文基于自动删除单元平均恒虚警(ACCA-CFAR)方法,在高斯混响背景下,提出了一种新的距离扩展目标CFAR检测方法。该方法将目标距离单元值通过模糊ACCA-CFAR映射到虚警空间上,获得相应的模糊隶属函数值,之后利用模糊积累准则对其进行积累,获得检测统计量。研究了模糊算数和、模糊代数和与模糊代数积三种积累准则,其中模糊算数和积累是首次分析,文中推导出其虚警概率的解析表达式。仿真结果表明,对于非起伏目标模型,模糊算数和、模糊代数和优于模糊代数积,而对于起伏目标,模糊代数积的积累性能更好;模糊ACCA-CFAR方法比现有的模糊OS-CFAR方法具有更好的抗干扰性能,尤其是干扰目标数超过理论上限时。

In order to improve the ability of the high resolution sonar to resist multi-target interferences in automatic detection, a CFAR detection scheme for range-extended targets based on automatic censoring cell averaging CFAR (ACCA-CFAR) method was proposed, which was suitable for Gauss reverberation background. It transformed the value of the target distance unit into the value of the fuzzy membership function mapped to the false alarm space by fuzzy ACCA-CFAR, and then used the fuzzy accumulation criterions to produce the detection statistic. The fuzzy arithmetic sum integrator, the fuzzy algebraic sum integrator and the fuzzy algebraic product integrator were considered. The fuzzy arithmetic sum integrator was first analyzed and the analytical expression of false alarm rate was derived. The simulation results show that the fuzzy algebraic product integrator has better performance in the fluctuating target model while the other two integrators have better performance in the non-fluctuating target model. The fuzzy ACCA-CFAR method has better anti-jamming performance than the existing fuzzy OS-CFAR method, especially when the number of jamming targets exceeds the theoretical upper limit.