A Cluster-Based Weighted Feature Similarity Moving Target Tracking Algorithm for Automotive FMCW Radar
This work addresses moving target detection and trajectory correction for autonomous vehicles, but it appears incremental as it builds on existing radar-based tracking methods.
The authors tackled the problem of moving target tracking in autonomous driving using millimeter-wave radar by proposing a weighted feature similarity algorithm for cluster matching, which improved matching rates under noise and interference, and demonstrated high recognition accuracy and low positional error in experiments.
We studied a target tracking algorithm based on millimeter-wave (MMW) radar in an autonomous driving environment. Aiming at the cluster matching in the target tracking stage, a new weighted feature similarity algorithm is proposed, which increases the matching rate of the same target in adjacent frames under strong environmental noise and multiple interference targets. For autonomous driving scenarios, we constructed a method that uses its motion parameters to extract and correct the trajectory of a moving target, which solves the problem of moving target detection and trajectory correction during vehicle movement. Finally, the feasibility of the proposed method was verified by a series of experiments in autonomous driving environments. The results verify the high recognition accuracy and low positional error of the method.