A novel approach to measuring the ground speed of autonomous vehicles using interferometric radar is presented. Using a microwave radar with interferometric processing and a feedforward neural network with local regression, high-accuracy velocity estimation is achieved with a downward-facing radar that, unlike forward-looking Doppler radars, can be protected from road debris and is, furthermore, unaffected by wheel slip and requires no external inputs, such as Global Navigation Satellite Systems (GNSS). A 16.9-GHz active interferometric array generates a grating lobe pattern and as the ground passes through the pattern, the range of frequencies over which the response is distributed increases proportionally with the ground velocity. We implement a feedforward neural network to estimate the velocity based on the interferometer’s frequency response. The estimator achieved a root-mean-squared error of 0.138 (m/s), equivalent to that of the forward-looking Doppler radars.

中文翻译：

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具有前馈神经网络的干涉式微波雷达用于车辆对地速度估计

提出了一种使用干涉雷达测量自动驾驶车辆地速的新方法。使用具有干涉测量处理的微波雷达和具有局部回归的前馈神经网络，通过向下的雷达实现高精度速度估计，与前视多普勒雷达不同，它可以免受道路碎片的影响，而且不受影响由车轮打滑，不需要外部输入，如全球导航卫星系统 (GNSS)。16.9 GHz 有源干涉阵列生成栅瓣图案，当地面穿过该图案时，响应分布的频率范围与地速成比例地增加。我们实现了一个前馈神经网络，以根据干涉仪的频率响应来估计速度。