Enhancing Target Acquisition in Long-Range Missiles Through Multi-Sensor Fusion

Long-range missiles cannot lock onto their target at launch. Instead, they rely on estimates of the line-of-sight between the missile and the target to orient their seeker head toward the target when entering seeker range. Whereas inertial navigation, which works by integrating the angular rates and specific forces measured by the inertial measurement unit, is sufficient for short ranges, sensor drift and initialization errors render this method unusable for longer ranges due to large errors in the line-of-sight. Therefore, a navigation algorithm is proposed that additionally uses measurements of the fire-control radar with respect to the missile and the target, as well as satellite navigation in a multiplicative extended Kalman filter to estimate the state of the missile and the target. Furthermore, the misalignment of the radar station is estimated through a novel recursive least squares estimator, thereby reducing the systematic errors in the radar measurement. Monte Carlo simulations show the increased performance of the proposed algorithm, ultimately resulting in a higher number of targets within the seeker’s instantaneous field of view. Further simulations demonstrate that the radar range with respect to the missile and the update frequency of the radar measurements can be reduced with almost no reduction in estimation quality. The scalability of the novel estimation method regarding the radar misalignment is proven by simulations with two radars.