Unbiased converted measurement manoeuvering target tracking under maximum correntropy criterion

Abstract

In this study, the manoeuvering target tracking problem is addressed by using the unbiased converted measurements from a two-dimensional radar system. Due to the fact that radar measurements are usually expressed in polar coordinates while the target motion is described in the Cartesian coordinates, the unbiased converted measurements are utilised to linearise the system model of the manoeuvering target tracking problem in the Cartesian coordinates. The manoeuver acceleration is modelled as the unknown input of the constant velocity kinematic model of the target. First, it is pointed out that the converted measurement noise no longer satisfies Gaussian distribution, even if the raw radar measurement noise is Gaussian noise. In order to solve the manoeuvering target tracking problem with non-Gaussian disturbances, a joint estimation method for the target state and the unknown input is presented under the maximum correntropy criterion. In the simulation, the proposed manoeuvering target tracking method is compared with the one developed on the basis of the traditional Kalman filter. The simulation results verify the effectiveness of the method proposed in this study.

Keywords

• radar tracking
• gaussian distribution
• target tracking
• gaussian noise
• maximum entropy methods
• estimation theory
• maximum correntropy criterion
• manoeuvering target tracking problem
• unbiased converted measurements
• cartesian coordinates
• raw radar measurement noise
• measurement noise conversion
• two-dimensional radar measurement system
• kalman filter
• gaussian distribution
• constant velocity kinematic model