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Mitigation of the Effects of Unknown Sea Clutter Statistics by Using Radial Basis Function Network

机译:利用径向基函数网络减轻未知海杂波统计的影响

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In this paper, we investigate feasibility of employing Radial Basis Function (RBF) network into non-coherent detection process, for detection of targets embedded in sea clutter of unknown statistics. We particularly have in mind Croatian part of Adriatic Sea, the local sea whose clutter statistic properties are not available in open literature. Performance of the detection process employing proposed RBF network is tested with simulated clutter samples based on real sea clutter data. These data were collected under sea state conditions that represent two thirds of the total wave heights in Adriatic and textcolor{red}{are} chosen to represent unknown clutter statistics due to the fact that no single probability density function equally well fits amplitude distribution of the range bins under test. It is demonstrated that, compared to the traditional [zlog(z)] method, RBF network with just four components and lognormal basis function, yields operating characteristics that better match designed ones.
机译:在本文中,我们研究了采用径向基函数(RBF)网络进入非相干检测过程的可行性,用于检测嵌入未知统计数据的海洋杂波的目标。我们特别考虑到亚得里亚海的克罗地亚部分,该地方的杂乱统计属性在公开文学中不提供。采用所提出的RBF网络的检测过程的性能用基于真正的海洋杂波数据的模拟杂波样本进行了测试。在海状态条件下收集这些数据,该条件代表亚得里亚类和TextColor {RED} {RED} {RED} {}的总波浪高度,因为没有单一概率密度函数同样适合幅度分布的事实,所选择的未知杂波统计正在测试的范围箱。据证明,与传统的[zlog(z)]方法相比,RBF网络具有仅为四个组件和逻辑正规基本功能,产生更好地匹配设计的操作特性。

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