In response to the target localization challenge in cooperative flight formations, this paper introduces a filtering algorithm for target localization based solely on angle measurements from passive radar sensors. The optimization algorithm, utilizing solved relative displacement results between aircraft and inertial navigation information, generates constraints for relative displacement between aircraft at each moment and constraints for aircraft inertial navigation at preceding and subsequent moments. By considering the relative position between aircraft and target as the optimization objective, a joint optimization model is established to solve the relative displacement between aircraft and target. The filtering method employs tightly coupled error state Kalman filtering, which couples and synchronously estimates the target state with the states of all aircraft in the cluster. Upon passive radar observations, error state measurements are updated, enabling accurate estimation of the target’s absolute position. Finally, through simulation experiments, the effectiveness and accuracy of each approach are validated across different simulation scenarios, proposing the optimal solution for accurately and reliably locating targets in scenarios featuring long distances, extensive ranges, and substantial radar errors within the aircraft cluster.