Micro-Doppler Parameter Estimation Using Variational Mode Decomposition With Finite Rate of Innovation

Shrikant Sharma, A. Girish, Darin Jeff, Garweet Sresth, Sanket Bhalerao, V. Gadre, C. Rao, P. Radhakrishna
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Abstract

The complete characterization of a target by radar involves estimation of its range and Doppler and micro-Doppler frequencies. Finite Rate of Innovation (FRI) approaches allow for sampling at sub-Nyquist rates. Empirical Mode Decomposition, which recursively decomposes a signal into different modes of unknown spectral bands, has performance limitations such as sensitivity to noise and sampling rates. These limitations are partially addressed by several variant algorithms; one of them is Variational Mode Decomposition (VMD), an entirely non-recursive model to extract the modes concurrently. In this paper, we propose an approach using FRI-based technique to estimate the delay of the target, and a VMD-based approach for Doppler and micro-Doppler parameter estimation. A novel mathematical analysis is proposed to identify the initialization parameters for faster convergence of the VMD algorithm. Further, we provide simulation results to show that the proposed approach is capable of estimating the parameters of multiple targets even in the presence of noise.
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有限创新率下变分模态分解微多普勒参数估计
雷达对目标的完整表征包括对其距离、多普勒和微多普勒频率的估计。有限创新率(FRI)方法允许以次奈奎斯特速率采样。经验模态分解(Empirical Mode Decomposition)是一种递归地将信号分解为未知频谱带的不同模态的方法,它存在对噪声的敏感性和采样率等性能限制。这些限制部分解决了几个不同的算法;其中之一是变分模态分解(VMD),这是一种完全非递归的模型,可以同时提取模态。在本文中,我们提出了一种基于复频的方法来估计目标的延迟,以及一种基于vmd的方法来估计多普勒和微多普勒参数。为了提高VMD算法的收敛速度,提出了一种新的数学分析方法来确定初始化参数。此外,我们提供的仿真结果表明,即使在存在噪声的情况下,该方法也能够估计多个目标的参数。
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