An IMM/EKF Approach for Enhanced Multitarget State Estimation for Application to Integrated Risk Management System

Abstract

For the generic assessment and the total management of collision risks with multitraffic in complex driving situations, it is essential to estimate and represent the target vehicles' overall behaviors such as heading, yaw rate, absolute velocity and acceleration, and relative position and relative velocity, which are the state of the target vehicle with respect to the host vehicle. To achieve this, this paper describes an interacting multiple model (IMM) approach using extended Kalman filters (EKFs) to improve multitarget state estimation performance with utilization of automotive radars. Automotive radar is the best fitted vehicular surround sensing technology with respect to functionality, robustness, reliability, dependence on weather conditions, etc. In an application of the automotive radar, the most important issue is to handle an uncertain measurement model problem that is wandering on the target's physical boundary. To cope with this problem, multimodels are formulated, and a new multitarget tracking algorithm is developed based on the IMM approach, global-nearest-neighbor-based data association, and the EKF method with elaborated modeling of automotive radar. The performance of the proposed multitarget tracking algorithm is verified via vehicle tests on real roads. Moreover, a performance comparison with a model-switching algorithm, which is a simple approach to handling the multimodel problem, has been conducted. It is shown that the target vehicle's overall behavior can be estimated by the proposed elaborated models, and the estimation performance can be significantly enhanced by the proposed IMM-based algorithm.