Classically, automatic detection of targets in active sonar system is addressed by a matched filter processing followed by a constant False Alarm Rate (CFAR) thresholding method. Even though, various CFAR techniques viz. CA CFAR, GO CFAR and SO CFAR etc. are available in literature, none of them alone is sufficient to eliminate the false echoes. In certain applications, such as active sonar where the probability of false alarm p_(fa) requirements are very stringent, the performance of CFAR alone cannot be used as detection criteria. Further, the choice of a particular CFAR algorithm is also a complex task, as the non-homogenous nature of the acoustic medium is difficult to predict. In this paper, a fusion algorithm is proposed for active sonar application where, in addition to CFAR technique, a support vector machines (SVM) based classification algorithm is also used to eliminate the false echoes. The performance of the algorithm is verified using practically measured data.
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机译：经典地，通过匹配的滤波器处理以及常量误报率（CFAR）阈值处理方法，通过匹配的滤波器处理来解决活动声纳系统中的目标的自动检测。即使，各种CFAR技术viz。 CA CFAR，GO CFAR等CFAR等在文献中提供，它们都不是足以消除假回声。在某些应用程序中，例如Active Sonar P_（FA）要求的概率非常严格，单独的CFAR的性能不能用作检测标准。此外，特定CFAR算法的选择也是一个复杂的任务，因为声介质的非均匀性难以预测。本文提出了一种用于有源声纳应用的融合算法，其中除CFAR技术外，基于支持向量机（SVM）的分类算法还用于消除误报。使用实际测量的数据验证算法的性能。
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