Distributed State Estimation with Unknown Input for Maneuvering Target

This paper is motivated by the desire to realize the cooperative detective of multiple missiles against a maneuvering target in the presence of unknown input. To this end, a consensus-based distributed algorithm within the fifth-degree cubature Kalman filter is proposed to estimate the state of the maneuvering target. The algorithm is built up according to the following steps: first, we rigorously construct the maneuvering target's mathematical model by combining the target's kinematics model and the aerodynamic model. Then, we creatively set up pseudo measurement using the state at the previous time step to improve the observability of the target's state. Next, to combat high system nonlinearity and high dimensional state estimation problems, a novel algorithm combining the distributed fifth-degree cubature Kalman filter and the unbiased minimum-variance estimation method is proposed. Finally, we use numerical simulations to demonstrate the effectiveness of our proposed algorithm.