attitude estimation

target tracking

measurement noise

noise covariance

IEEE Transactions on Signal Processing

In this study, we propose a novel extended target tracking algorithm which is\ncapable of representing the extent of dynamic objects as an ellipsoid with a\ntime-varying orientation angle. A diagonal positive semi-definite matrix is\ndefined to model objects' extent within the random matrix framework where the\ndiagonal elements have inverse-Gamma priors. The resulting measurement equation\nis non-linear in the state variables, and it is not possible to find a\nclosed-form analytical expression for the true posterior because of the absence\nof conjugacy. We use the variational Bayes technique to perform approximate\ninference, where the Kullback-Leibler divergence between the true and the\napproximate posterior is minimized by performing fixed-point iterations. The\nupdate equations are easy to implement, and the algorithm can be used in\nreal-time tracking applications. We illustrate the performance of the method in\nsimulations and experiments with real data. The proposed method outperforms the\nstate-of-the-art methods when compared with respect to accuracy and robustness.\n

Random Matrix Based Extended Target Tracking With Orientation: A New Model and Inference