The application belongs to the field of unmanned aerial
vehicle positioning, and particularly relates to an unmanned aerial vehicle ultra-
wideband fine positioning method based on
artificial noise excitation. Firstly, aiming at the unmanned aerial vehicle ultra-
wideband and dynamic
information fusion positioning problem, a nonlinear
positioning system containing an unmanned aerial vehicle motion
state model and an ultra-
wideband measurement model is established, and a
skew t distribution is introduced to describe the statistical characteristics of the ultra-wideband measurement error; secondly, a filtering
algorithm is designed,
artificial noise is introduced to the measurement residual in the measurement update process, the residual neighborhood sample is constructed, and the robust
scale weight calculated from the residual sample is averaged to obtain the
noise enhanced
scale weight, which is used for adaptive correction of the measurement update process; finally, the
noise enhanced
scale weight is used to complete the recursive
estimation of the unmanned aerial vehicle state, the measurement update step is executed in a loop, and the unmanned aerial vehicle position
estimation result is output in real time. It is suitable for unmanned aerial vehicle ultra-wideband positioning systems under complex interference conditions such as non-line-of-
sight error,
multipath effect and external disturbance.