毫米波雷达微弱行人轨迹跟踪-预测一体化方法

针对城市道路场景微弱行人目标雷达回波信号极易被强背景杂波淹没,导致目标轨迹跟踪及预测失效难题,提出了一 种基于调频连续波-多输入多输出毫米波雷达的微弱行人轨迹跟踪-预测一体化方法。 首先,利用递归贝叶斯检测前跟踪算 法,直接从未经阈值处理的雷达三维原始频谱数据中提取目标运动轨迹,解决了传统阈值决策信息丢失带来的跟踪性能下降问 题,并在此基础上提出了一种基于 Transformer 的端到端轨迹预测模型,进一步挖掘隐藏在跟踪轨迹中的时空相关性,完成了微 弱行人目标轨迹的精准预测。 实验结果表明,方法在信噪比大于-20 dB 时,预测轨迹的平均位移误差和最终位移误差分别小 于 0. 706、1. 215 m,均优于高斯过程、长短期记忆网络等传统方法。

Integrated trajectory tracking and prediction method for weak pedestrian with millimeter wave radar

The radar echoes from weak pedestrians are easily submerged by strong background clutters in urban road traffic scenarios, resulting in the failure of trajectory tracking and prediction. To address this problem, this article proposes an integrated trajectory tracking and prediction method for weak pedestrians based on frequency-modulated continuous wave-multiple input multiple output millimeter wave radar. Firstly, the recursive Bayesian track-before-detect algorithm is utilized to directly extract target motion trajectory from the non-thresholding three-dimensional radar raw spectrum, which avoids the tracking performance degradation caused by the information loss from the traditional threshold-decision process. On this basis, this article proposes a Transformer-based end-to-end trajectory prediction model to further explore the spatiotemporal correlations of tracking trajectory and achieves accurate trajectory prediction of a weak pedestrian. Experimental results show that, when the signal-to-noise ratio is greater than - 20 dB, the average displacement error and final displacement error of the predicted trajectory of the proposed method are less than 0. 706 and 1. 215 m, respectively, which are all superior to traditional methods such as Gaussian process and long short-term memory network.