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Robust detection of weak signals in a high-clutter environment

机译:在高杂波环境中对弱信号的鲁棒检测

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Abstract: This paper will present a new algorithm for determining the presence or absence of a weak signal in non-Gaussian noise. A weak signal is one that is vanishingly small compared to the noise disturbance. There are several applications in which detecting a weak signal is important. If the weak signal is the result of the reflection of a small target then accurate detection can indicate target presence. If the target is maneuvering and measurements can only be made at widely separated fixed intervals, then after target detection an estimate of target velocity can be made. The algorithm presented in this paper is no more structurally complex than the LOD, yet possesses several important advantages over the LOD. The fundamental advantage is the fact that the underlying noise statistics do not have to be known a prior. In addition, whereas the LOD may require a rather complex nonlinearity to preprocess the data, the algorithm presented here does not. This paper will develop the algorithm, and then report on simulation testing that was performed to ascertain its performance. It will be shown that the proposed algorithm performs significantly better (that is, is able to detect the presence of weak targets) than more conventional linear detection methods (Wiener filtering).!10
机译:摘要:本文将提出一种新算法,用于确定非高斯噪声中是否存在弱信号。与噪声干扰相比,微弱的信号几乎消失了。在一些应用中,检测弱信号很重要。如果弱信号是小目标反射的结果,则准确检测可以表明目标的存在。如果目标是机动的,并且只能在相距很远的固定间隔内进行测量,则在目标检测后即可估算目标速度。本文介绍的算法在结构上没有LOD复杂,但比LOD具有几个重要的优势。基本优点是无需事先知道基础噪声统计信息。另外,尽管LOD可能需要相当复杂的非线性来预处理数据,但此处介绍的算法却不需要。本文将开发该算法,然后报告为确定其性能而进行的仿真测试。将显示,与更常规的线性检测方法(维纳滤波)相比,所提出的算法的性能明显更好(也就是说,能够检测到弱目标的存在)。10

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