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Comparison of Adaptive Spectral Estimation for Vehicle Speed Measurement with Radar Sensors

机译:雷达传感器测量车速的自适应光谱估计的比较

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Vehicle speed-over-ground (SoG) radar offers significant advantages over conventional speed measurement systems. Radar sensors enable contactless speed measurement, which is free from wheel slip. One of the key issues in SoG radar is the development of the Doppler shift estimation algorithm. In this paper, we compared two algorithms to estimate a mean Doppler frequency accurately. The first is the center-of-mass algorithm, which based on spectrum center-of-mass estimation with a bandwidth-limiting technique. The second is the cross-correlation algorithm, which is based on a cross-correlation technique by cross-correlating Doppler spectrum with a theoretical Gaussian curve. Analysis shows that both algorithms are computationally efficient and suitable for real-time SoG systems. Our extensive simulated and experimental results show both methods achieved low estimation error between 0.5% and 1.5% for flat road conditions. In terms of reliability, the cross-correlation method shows good performance under low Signal-to-Noise Ratio (SNR) while the center-of-mass method failed in this condition.
机译:车载地面速度(SoG)雷达比常规速度测量系统具有明显的优势。雷达传感器可实现无接触速度测量,而不会出现车轮打滑。 SoG雷达的关键问题之一是多普勒频移估计算法的发展。在本文中,我们比较了两种算法以准确估计平均多普勒频率。第一个是质量中心算法,它基于带带宽限制技术的频谱质量中心估计。第二种是互相关算法,该算法基于互相关技术,通过将多普勒频谱与理论高斯曲线互相关。分析表明,这两种算法都具有高效的计算能力,适用于实时SoG系统。我们广泛的模拟和实验结果表明,两种方法在平坦路况下均实现了0.5%至1.5%的低估计误差。在可靠性方面,互相关方法在低信噪比(SNR)下显示出良好的性能,而质心方法在这种情况下失败了。

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