Fading Unscented–Extended Kalman Filter for Multiple Targets Tracking with Symmetric Equations of Nonlinear Measurements

This paper is devoted to the problem of multiple targets tracking based on symmetric equations of nonlinear measurements. We develop a nonlinear stochastic model with unknown random bias to provide a unified structure for the tracking systems with different types of symmetric measurement equations. Moreover, the fading unscented–extended Kalman filter (FUEF) is designed to deal with the strong nonlinearities by embedding the unscented transform into the extended Kalman filter and to conduct the effect of unknown bias by inserting the fading factor. The performance of the novel filter paired with two of symmetric measurement equations are illustrated and compared by the Monte Carlo simulation results.