Extended Object Tracking Under a State-Coupled Model with Gaussian Mixture Distribution
Zhifei Li, Chunsheng Liu, Shuli Ma, Hongyan Wang · 2024
This work proposes a state-coupled model (SCM) for extended object tracking, which treats the orientation and velocity as two dependent variables. With this model, the distribution of multiple measurements is modeled via Gaussian mixture density to match the actual automotive radar or Lidar data. As a result, SCM becomes a highly nonlinear model with multiplicative noise. To handle this challenge, we use the deterministic sampling approach to update the kinematics and orientation information, followed by a constraint condition. And the extent parameters are estimated under a Bayesian framework with pseudo-measurements. An evaluation is conducted on simulated data, which illustrates that the proposed model and filter are effective.