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Radon-Linear Canonical Ambiguity Function-Based Detection and Estimation Method for Marine Target With Micromotion

机译:基于线性经典歧义函数的海洋运动目标检测与估计方法

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摘要

Robust and effective detection of a marine target is a challenging task due to the complex sea environment and target's motion. A long-time coherent integration technique is one of the most useful methods for the improvement of radar detection ability, whereas it would easily run into the across range unit (ARU) and Doppler frequency migration (DFM) effects resulting distributed energy in the time and frequency domain. In this paper, the micro-Doppler (m-D) signature of a marine target is employed for detection and modeled as a quadratic frequency-modulated signal. Furthermore, a novel long-time coherent integration method, i.e., Radon-linear canonical ambiguity function (RLCAF), is proposed to detect and estimate the m-D signal without the ARU and DFM effects. The observation values of a micromotion target are first extracted by searching along the moving trajectory. Then these values are carried out with the long-time instantaneous autocorrelation function for reduction of the signal order, and well matched and accumulated in the RLCAF domain using extra three degrees of freedom. It can be verified that the proposed RLCAF can be regarded as a generalization of the popular ambiguity function, fractional Fourier transform, fractional ambiguity function, and Radon-linear canonical transform. Experiments with simulated and real radar data sets indicate that the RLCAF can achieve higher integration gain and detection probability of a marine target in a low signal-to-clutter ratio environment.
机译:由于复杂的海洋环境和目标的运动,对海洋目标进行稳健而有效的检测是一项艰巨的任务。长期相干积分技术是提高雷达探测能力的最有用的方法之一,而它很容易遇到跨距离单位(ARU)和多普勒频率偏移(DFM)的影响,从而导致时间和能量的分布频域。在本文中,海洋目标的微多普勒(m-D)签名用于检测并建模为二次调频信号。此外,提出了一种新颖的长期相干积分方法,即Radon-线性规范歧义函数(RLCAF),以检测和估计没有ARU和DFM效应的m-D信号。首先,通过沿移动轨迹进行搜索来提取微运动目标的观测值。然后,使用长时间瞬时自相关函数执行这些值以降低信号阶数,并使用额外的三个自由度在RLCAF域中进行很好的匹配和累积。可以证明,提出的RLCAF可以看作是流行歧义函数,分数阶傅里叶变换,分数歧义函数和Radon线性规范变换的推广。模拟和真实雷达数据集的实验表明,RLCAF在低信杂比环境下可以实现更高的集成增益和对海洋目标的探测概率。

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