A new on-line systematic errors registration method
Lin Zhou, Quan Pan, Yan Liang, Jie Zhou · 2013
In complex surveillance system, it is important to register sensor measurement with systematic errors. If measurements are not corrected, it leads to degradation in track accuracy. It is vital to estimate systematic errors, especially, it is necessary to estimate systematic errors with unknown prior knowledge. In this paper, a novel registration method named EX-UI (exact-unknown input) is proposed to estimate systematic errors. Firstly, we transform measurements from sensors and target state into pseudomeasurements, and utilize exact method (EX) method to conceive system including pseudomeasurement model and systematic errors model with unknown input (UI). Next, we design decoupled filter based on above system. Finally, the systematic errors are estimated by minimum variance unbiased (MVU) theory. Simulation results demonstrate that the systematic errors with unknown prior knowledge can be exactly estimated using proposed method, and it is convergent and outperforms other method.