Extended Target-Tracking Algorithm Based on Variational Bayes and Axis Estimation Theory

Abstract

Aiming at resolving the problem of low tracking accuracy for maneuvering extended targets in lidar systems, an interactive multimodel variational Bayes independent axis estimation (IMM-VB-IAE) algorithm is proposed in this article. First, the algorithm utilizes IMM to adaptively select the appropriate model to track the target according to the changes in the target’s motion state, thereby improving the overall tracking performance. Second, the algorithm uses VB theory to approximate the posterior closed expression, which simplifies the solution process by transforming the original complex inference problem into a parameter optimization problem. Finally, the principle of eigendecomposition is utilized to quadratically estimate the ellipse axes’ lengths, which improves the estimation accuracy of the ellipse parameters. The final simulation and experimental results demonstrate that the proposed algorithm outperforms several other algorithms in the accuracy of tracking maneuvering extended targets, with average OSPA and Gaussian-Wasserstein distances reduced by at least 55.5% and 56.1%, respectively.