Recursive update filtering-based variational Bayesian approach for extended target or group target tracking with nonlinear measurements

Abstract

For extended target and group target tracking, most existing random matrix approaches assume that the measurements are linear in the kinematic state and in the noise with its covariance being a random matrix to represent the extension of an extended target or target group. However, in practice, the measurements (e.g., range and bearing) are nonlinear in the kinematic state and extension noise. Only the matched linearization (ML) approach has been proposed to linearize the nonlinear measurement function based on minimum mean square error criterion. However, if the state prediction error is not small, it will incur large linearization error and then the variational Bayesian measurement update approach produces inaccurate estimation results. To this point, using the ML technology, we present a recursive update filtering-based variational Bayesian update (RUF-VBU) approach for better estimation performance. In addition, the square-root implementation of RUF-VBU is proposed for the recursive interruption problem caused by the nonpositive definite covariance matrix. The simulation results demonstrate the effectiveness of the proposed approach.