Unmanned Aerial Vehicle's State Estimation with Multiple Unmanned Ground Vehicles Cooperative Observation Based on Set-Membership Filter

摘要

In this paper, the state estimation problem of Unmanned Aerial Vehicle (UAV) with multiple Unmanned Ground Vehicles (UGV) cooperative observation is researched. In traditional observation systems, there are usually problems with low observation accuracy or robustness and prior assumptions about noises. For the aim of improving the accuracy and robustness of state estimation results of UAV, Set-Membership Filter (SMF) method is applied to the cooperative observation system. Unlike the other algorithms requiring the measurement and process error to obey the Gaussian distribution with zero mean, such as Kalman filter or Particle filter, SMF method only assumes that the measurement errors are modeled as Unknown-But-Bounded (UBB) set, which can be easily obtained and applied in the real cases. The application of SMF algorithm in single UGV observation system and multiple UGVs cooperative observation system with obstacles are researched respectively. Considering the existence of obstacles, the state of UAV can still be successfully estimated when the observation data lost. Finally, the single UGV and multiple UGVs cooperative observation experiments are conducted to verify the accuracy and effectiveness of SMF method.