Nonlinear maneuvering non-ellipsoidal extended object tracking using random matrix

The random-matrix approach to extended object tracking (EOT) supposes that the measurement model is linear and its covariance is a random matrix to stand for the object extension. In practice, however, most measurement algorithms are nonlinear and multiple extensions cannot be simplified by an ellipsoid. This paper proposes a new method for nonlinear maneuvering non-ellipsoidal extended object tracking (non-MNEOT) based on the two problems above in practice. Firstly, the stochastic model approximation represented by matched linearization is used to solve the nonlinear measurement model and the linearized measurements are applied to the random-matrix algorithms to the EOT after a simple conversion. Then, multiple sub-objects are used to approximate the non-ellipsoidal target and applied to the extended object tracking by a combination of random matrix. Lastly, most targets in practice are maneuvering targets, so we introduce the interactive multiple model (IMM) to nonlinear non-ellipse EOT. The effectiveness of the approach in this paper is demonstrated by simulation results.