Research on Invalid Target Filtering and Target Tracking Algorithm Optimization in Millimeter-Wave Radar Technology

The application of millimeter-wave radar technology in autonomous driving has become increasingly widespread with the rapid development of intelligent transportation systems. However, millimeter-wave radar is easily affected by environmental noise, multipath reflections, and electromagnetic interference, resulting in a large number of invalid target signals that reduce the system’s detection accuracy and safety. We proposes a method for filtering invalid targets based on interference signal characteristics and an Adaptive Interactive Multiple Model Kalman Filter (IMM-KF) target tracking algorithm. First, we effectively filter out empty targets, ghost targets, and false targets through a threshold method and lifecycle assessment, achieving a filtering rate exceeding 99.8%. Second, the improved Adaptive IMM-KF algorithm, combined with the Hungarian algorithm, associates and tracks multiple targets. The root mean square error (RMSE) of our methods is reduced by 7.07% and 8.05% compared to the traditional IMM and Unscented Kalman Filter (UKF) algorithms in scenarios with a single pedestrian moving in a straight line. And in scenarios involving both pedestrians and motor vehicles, the RMSE is reduced by 4.29% and 7.61% compared to the traditional IMM and UKF algorithms, respectively. Real-vehicle experiments have validated the robustness and accuracy of the proposed method across various scenarios.