The application discloses a
label multi-Bernoulli tracking method for cooperative and non-cooperative unmanned aerial vehicles, and comprises the following steps: acquiring a
current time unmanned aerial vehicle measurement set, a cooperative unmanned aerial vehicle report set and a basic
state vector; introducing a discrete mode variable to establish a Markov jump mechanism to obtain an extended state and a motion
state function thereof; adopting a
label multi-
Bernoulli filter to track multiple unmanned aerial vehicles to obtain corresponding prediction densities; obtaining a posterior density of the cooperative unmanned aerial vehicle and a posterior density of the non-cooperative unmanned aerial vehicle according to the measurement set, the cooperative unmanned aerial vehicle report set and the prediction densities; introducing a trajectory quality evaluation mechanism to adjust the trajectory quality to obtain a final posterior density of the non-cooperative unmanned aerial vehicle; and taking the posterior density of the cooperative unmanned aerial vehicle and the posterior density of the non-cooperative unmanned aerial vehicle as the output on one hand to obtain a
current time unmanned aerial vehicle target set, and taking the combination thereof as the input of a next time prediction step on the other hand to start a new round of filtering circulation.