Maneuvering target passive tracking based on adaptive extended H∞ filter

To improve the performance of maneuvering target tracking, an interacting multiple model (IMM) algorithm is proposed based on the adaptive extended H∞ filter. The improved Sage-Husa adaptive filter is intergrated with the IMM model to fuse the measurements of multiple passive sensors to alleviate the unobservability and the nonlinearity simultaneously. The extended H∞ filter works as the model-conditional filter. Its parameters and observed noise predicted covariance matrix are adjusted for robustness. The simulation results show that the extended H∞ fusion tracking algorithm has higher tracking precision than the extended Kalman filter interacting multiple model and the traditional interacting multiple model. The extended H∞ fusion tracking algorithm is an effective tracking algorithm for the multiple stations passive infrared search and tracking system.