论文标题
用MMWave雷达跟踪人类跟踪：一种深度学习方法，具有不确定性估计
Human Tracking with mmWave Radars: a Deep Learning Approach with Uncertainty Estimation
论文作者
论文摘要
MMWave Radars最近引起了极大的关注，作为在室内环境中追踪人类运动的一种手段。当基础运动高度非线性或呈现长期的时间依赖性时，广泛采用的Kalman滤波器跟踪方法会经历性能下降。作为解决方案，在本文中，我们设计了一个卷积转变的神经网络（NN），该神经网络学会了从高维雷达数据中准确估计受监测受试者的位置和速度。 NN使用高斯负模具可能性损失函数训练为概率模型，并以时间变化的误差协方差矩阵的形式在其输出处获得明确的不确定性估计。使用77 GHz FMCW雷达进行彻底的实验评估。拟议的架构除了允许一个人衡量跟踪过程中的不确定性外，还导致了与文献的最佳方法（即卡尔曼过滤）的最佳方法，从32.8 cm到7.59 cm的平均误差，从56.8 cm/s分别从位置和VELOCITY跟踪。
mmWave radars have recently gathered significant attention as a means to track human movement within indoor environments. Widely adopted Kalman filter tracking methods experience performance degradation when the underlying movement is highly non-linear or presents long-term temporal dependencies. As a solution, in this article we design a convolutional-recurrent Neural Network (NN) that learns to accurately estimate the position and the velocity of the monitored subjects from high dimensional radar data. The NN is trained as a probabilistic model, utilizing a Gaussian negative log-likelihood loss function, obtaining explicit uncertainty estimates at its output, in the form of time-varying error covariance matrices. A thorough experimental assessment is conducted using a 77 GHz FMCW radar. The proposed architecture, besides allowing one to gauge the uncertainty in the tracking process, also leads to greatly improved performance against the best approaches from the literature, i.e., Kalman filtering, lowering the average error against the ground truth from 32.8 to 7.59 cm and from 56.8 to 14 cm/s in terms of position and velocity tracking, respectively.
