摘要
A second-order divided difference filter (SDDF) is derived for integrating line of sight measurement from vision sensor with acceleration and angular rate measurements of the follower to estimate the precise relative position,velocity and attitude of two unmanned aerial vehicles (UAVs).The second-order divided difference filter which makes use of multidimensional interpolation formulations to approximate the nonlinear transformations could achieve more accurate estimation and faster convergence from inaccurate initial conditions than standard extended Kalman filter.The filter formulation is based on relative motion equations.The global attitude parameterization is given by quarternion,while a generalized three-dimensional attitude representation is used to define the local attitude error.Simulation results are shown to compare the performance of the second-order divided difference filter with a standard extended Kalman filter approach.
A second-order divided difference filter （SDDF） is derived for integrating line of sight measurement from vision sensor with acceleration and angular rate measurements of the follower to estimate the precise relative position, velocity and attitude of two unmanned aerial vehicles （UAVs）. The second-order divided ditterence filter which makes use of multidimensional interpolation formulations to approximate the nonlinear transfor- mations could achieve more accurate estimation and faster convergence from inaccurate initial conditions than standard extended Kahnan filter. The filter formulation is based on relative motion equations. The global attitude parameterization is given by quarternion, while a generalized three-dimensional attitude representation is used to define the local attitude error. Simulation results are shown to compare the performance of the secondorder divided difference filter with a standard extended Kalman filter approach.
基金
Sponsored by the Aerospace Technology Innovation Funding(Grant No. CASC0209)
