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首页> 外文期刊>IEEE sensors journal >Low-Complexity Joint Extrapolation-MUSIC-Based 2-D Parameter Estimator for Vital FMCW Radar
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Low-Complexity Joint Extrapolation-MUSIC-Based 2-D Parameter Estimator for Vital FMCW Radar

机译：基于低复杂度联合外推-MUSIC的FMCW雷达二维参数估计器

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摘要

In this paper, a low-complexity joint extrapolation-multiple signal classification (MUSIC)-based 2-D parameter estimator is proposed for vital frequency-modulated continuous-wave (FMCW) radar. Recently, an FMCW radar, which can detect the distance and vital Doppler information, has been considered for vital non-contact radar. In the conventional FMCW radar system, fast Fourier transform (FFT)-based algorithms with low complexity are used to extract multiple parameters. However, the resolution and accuracy of an FFT-based parameter estimator are considerably low. Thus, 2-D high-resolution algorithms, such as the 2-D estimation of signal parameters via rotational invariance techniques and 2-D MUSIC, have been suggested as an alternative method. However, a large computation power is required compared with the FFT-based methods. Therefore, this paper proposes a 2-D parameter estimator that combines extrapolated FFT and MUSIC to reduce the computational load for vital detection. The proposed method uses an extrapolated FFT to overcome the disadvantages of the low-resolution FFT for the distance information, and then, the 1-D MUSIC algorithm is applied to the Doppler domain direction only for the extracted magnitude and phase information of the target's extrapolated FFT results. Hence, the proposed algorithm combines the advantages of FFT and MUSIC. The performance of the proposed estimation is compared with that of other algorithms using Monte Carlo simulation results. The root-meansquare error of the proposed method is compared with that of 2-D MUSIC with various parameters. To verify the performance of the proposed combination method, the FMCW radar was used, and its performance was verified in an indoor environment.
机译：本文提出了一种基于低复杂度联合外推多信号分类（MUSIC）的二维参数估计器，用于生命调频连续波（FMCW）雷达。近来，已经考虑将FMCW雷达用于重要的非接触式雷达，该雷达可以检测距离和重要的多普勒信息。在常规的FMCW雷达系统中，具有低复杂度的基于快速傅里叶变换（FFT）的算法用于提取多个参数。但是，基于FFT的参数估计器的分辨率和准确性非常低。因此，已经提出了2-D高分辨率算法，例如经由旋转不变性技术和2-D MUSIC的信号参数的2-D估计，作为替代方法。但是，与基于FFT的方法相比，需要很大的计算能力。因此，本文提出了一种结合外推FFT和MUSIC的二维参数估计器，以减少生命体检的计算量。所提出的方法使用外推FFT来克服距离信息的低分辨率FFT的缺点，然后将一维MUSIC算法仅用于提取目标外推的幅度和相位信息的多普勒域方向FFT结果。因此，所提出的算法结合了FFT和MUSIC的优点。使用蒙特卡洛模拟结果，将所提出的估计的性能与其他算法的性能进行比较。将所提方法的均方根误差与具有各种参数的二维MUSIC的均方根误差进行了比较。为了验证所提出的组合方法的性能，使用了FMCW雷达，并在室内环境下对其性能进行了验证。

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