Tracking of Extended Object Using Random Matrix With Non-Uniformly Distributed Measurements

Extended object tracking (EOT) is gaining momentum in recent years. The random matrix method is a popular EOT method, which has a simple yet effective framework. The existing random matrix approaches usually assume that scatter centers are uniformly or symmetrically distributed on the object. However, due to weak signal strength and limited measuring angles, scatter centers are densely distributed on several (separated) parts rather than uniformly distributed on the whole object. This results in several measurement-densely-distributed parts. To address this problem, this paper proposes a new conditional Gaussian mixture model. Each component of the model characterizes a single measurement-densely-distributed part. The object extension is modeled as an ellipse by a random matrix. As a portion of the object, each measurement-densely-distributed part is modeled by scaling and shifting that ellipse and transforming that random matrix. This modeling can effectively describe the non-uniformly distributed measurements, but also inherit the simplicity of the random matrix model. Based on the new model and utilized the random matrix framework, a variational Bayesian approach is derived, which recursively and efficiently estimates kinematic and extension states. From the simulation and real experimental results, the proposed approach has a significant performance improvement over some existing random matrix approaches.

Automated summary

Extended object tracking (EOT) has gained attention recently, with the random matrix method being a popular and effective framework. However, existing methods assuming uniformly distributed scatter centers on the object fail to address the issue of densely distributed scatter centers on separated parts. To solve this, a new conditional Gaussian mixture model is proposed to accurately model the non-uniform distribution of measurements. By incorporating this new model into the random matrix framework, a variational Bayesian approach is derived for recursively estimating kinematic and extension states with improved performance.