A Novel Cooperative Navigation Method Applied to Single-Leader Mode With Insufficient Observation Information
Yang Pang, Xiong Pan, Ningfang Song, Qingzhong Cai · IEEE Transactions on Industrial Informatics · 2024
In single-leader mode, if only relative ranging information is available between drones, the traditional cooperative navigation (CN) algorithm suffers from the problem of incomplete observability of the position state quantity. To address this problem, this article describes the CN process using an objective optimization function, and adds the constraints of time-series information of inertial trajectories and relative spatial relationship to the objective function, which narrows the solution space of the position state quantity. Then, for realizing the decoupling of the optimization objective function, this article further simplifies the CN process into an optimization problem that only needs to compute two parameters such as the optimal rotation matrix and the optimal translation matrix. Second, this article proposes a concept of planal gridding and adopts the traversal search instead of the traditional partial differential calculation of the extrema to obtain these two key parameters, which simplifies the calculation process of the model. Finally, the process of noise reduction for CN results is given. Simulation results show that the proposed method can suppress the divergence of navigation error of follower drones equipped with different levels of inertial navigation systems (INSs). The higher the accuracy of INS, the better the error suppression effect. In a flight experiment of about 700 s, the proposed method resulted in a CN accuracy of better than 250 m for a follower drone, which was equipped with an INS with a gyro bias stability of 8°/h.