Browse Publications Technical Papers 2020-01-0117

IMM-KF Algorithm for Multitarget Tracking of on-Road Vehicle 2020-01-0117

Tracking vehicle motion trajectories is essential for autonomous vehicles and advanced driver-assistance systems to understand traffic environment and evaluate collision risk. In order to reduce the position deviation and fluctuation of tracking on-road vehicle by millimeter-wave radar (MMWR), an adaptive interactive multi-model Kalman filter (IMM-KF) tracking algorithm including data association and track management is proposed. In general, it is difficult to model the target vehicle accurately due to lack of vehicle kinematics parameters, like wheel base, uncertainty of driving behavior and limitation of sensor’s field of view. To handle the uncertainty problem, an interacting multiple model (IMM) approach using Kalman filters is employed to estimate multitarget’s states. Then the compensation of radar ego motion is achieved, since the original measurement is under the radar coordinate system. In addition, an adaptive Kalman filter is engaged to handle the uncertainty of radar measurement noise and process noise. Taking into account the real-time performance of the algorithm and the distinguishability of vehicles involved in traffic, the nearest neighbor data association (NNDA) is used to associate observation with trajectory, which is fast and stable. And after the process of track establishment, confirmation, continuous updating, supplement and extinction, the multi-track management of vehicle targets is realized. Finally, three normal traffic scenarios including straight driving, overtaking, turning are designed to test the feasibility and validity of the tracking algorithm on PreScan simulation platform. And then real vehicle experiment is executed in the three typical conditions by using test vehicle which are equipped with MMWR and High Precision Positioning System. Both simulation and experimental results show that the algorithm has higher accuracy and stability than original radar observation.


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