Multi-photoelectric tracking systems (MPTSs) provide high-precision line-of-sight (LOS) angles for long-range passive tracking, with each photoelectric tracking system (PTS) delivering bearing-only measurements. In practice, image-based target extraction and optical–mechanical pointing may suffer from intermittent errors when highly maneuverable targets are observed, thereby degrading tracking accuracy. To address this issue, we consider a time-varying uncertain dynamics model with a lumped uncertainty and its temporal difference. A centralized twice-extended state Kalman filter (CTESKF) is proposed to augment the kinematic state with the lumped uncertainty and its temporal difference, yielding a Kalman-type recursion with a computable covariance upper bound. Building on this, a diffusion-based twice-extended state Kalman filter (DTESKF), which combines local updates with single-round covariance-intersection diffusion fusion, is proposed to achieve distributed filtering with limited communication cost. Simulation results show that CTESKF and DTESKF achieve competitive accuracy–efficiency trade-offs in a weakly nonlinear setting and a 3D bearing-only MPTS scenario.
Li et al. (Thu,) studied this question.