基于改进VI-CFAR的模糊距离扩展目标检测算法  

Range-Extended Target Detection Algorithm Based on Improved VI-CFAR

作　　者：张胜男 殷超然 徐达[2] ZHANG Shengnan;YIN Chaoran;XU Da(School of Microelectronics,University of Chinese Academy of Sciences,Beijing,100049,China;Key Laboratory of Underwater Vehicle Information Technology,Institute of Acoustics,Chinese Academy of Sciences,Beijing,100190,China)

机构地区：[1]中国科学院大学微电子学院,北京100049 [2]中国科学院声学研究所水下航行器信息技术重点实验室,北京100190

出　　处：《网络新媒体技术》2022年第2期42-49,共8页Network New Media Technology

基　　金：国家自然科学基金项目(编号:61701489)。

摘　　要：为了提高主动声呐在高斯背景下对距离扩展目标的检测能力,本文提出了一种新的恒虚警(CFAR)检测方法。该方法首先利用最小选择无偏最小方差和单元平均(UMCASO)方法对可变指数恒虚警(VI-CFAR)进行改进,得到改进VI-CFAR方法,再将其用于对距离扩展目标检测的第一门限处理中,同时第二门限处理使用模糊积累方法。仿真结果表明,该方法在均匀背景下只有较小的CFAR损失,而在多目标干扰下相比传统方法有更强的鲁棒性,对抗多目标干扰性能优越。对于起伏目标模型,模糊代数积积累准则性能优于模糊代数和积累准则。在不同的背景下,选用与之相适应的积累准则,可获得较为理想的检测性能。In order to improve the ability of active sonar to detect extended range targets in Gaussian background,this paper proposes a new constant false alarm(CFAR)detection method.This method first uses the minimum selection unbiased minimum variance and unit average(UMCASO)method to improve the variable exponential constant false alarm(VI-CFAR),and obtains the improved VI-CFAR method,which is then used for the detection of distance-extended targets In the first threshold processing,while the second threshold processing uses the fuzzy accumulation method.The simulation results show that this method has only a small CFAR loss under uniform background,and it is more robust than traditional methods under multi-target interference,and has superior performance against multi-target interference.For fluctuating target models,the performance of fuzzy algebraic product accumulation criterion is better than fuzzy algebra and accumulation criterion.Under different backgrounds,the appropriate accumulation criterion can be selected to obtain a more ideal detection performance.

关 键 词：距离扩展目标 模糊积累 恒虚警检测 可变性指数 抗多目标干扰 

分 类 号：U666.7[交通运输工程—船舶及航道工程]