Aiming at the adverse effect caused by limited detecting probability of sensors on filtering precision of a nonlinear system state,a novel multi-sensor federated unscented Kalman filtering algorithm is proposed.Firstly,combined with the residual detection strategy,effective observations are correctly identified.Secondly,according to the missing characteristic of observations and the structural feature of unscented Kalman filter,the iterative process of the single-sensor unscented Kalman filter in intermittent observations is given.The key idea is that the state estimation and its error covariance matrix are replaced by the state one-step prediction and its error covariance matrix,when the phenomenon of observations missing occurs.Finally,based on the realization mechanism of federated filter,a new fusion framework of state estimation from each local node is designed.And the filtering precision of system state is improved further by the effective management of observations missing and the rational utilization of redundancy and complementary information among multi-sensor observations.The theory analysis and simulation results show the feasibility and effectiveness of the proposed algorithm.

Multi-sensor federated unscented Kalman filtering algorithm in intermittent observations

This paper presents a study of the error model based on image perception and target positioning. The interactive multi-model unscented Kalman filter algorithm for single-UAV target perception and the federated filter algorithm for multi-UAV fusion are used to achieve multi-UAV positioning fusion. Considering the effective performance of the filtering algorithm, the adaptive motion model and fusion feedback factors are established. The effectiveness of the algorithm is verified by experimental results.

A Fusion Estimation Method for Multi-UAV Geosynchronous Target Perception

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A Fusion Estimation Method for Multi-UAV Geosynchronous Target Perception