Target tracking strategy of unmanned surface vehicle based on relative time-varying tracking position

Objective Target tracking is an important application of unmanned surface vehicles (USVs). This study proposes a relative time-varying tracking position (RTTP) strategy to improve the tracking stability and address the problem that the reference trajectory obtained by the relative fixed tracking position (RFTP) strategy contains inflection points and leads to tracking instability.

Methods A first-order hysteresis filter is used to process the target USV's heading variation. The time-varying tracking position is then designed according to the filtered data, the target tracking problem is transformed into a trajectory tracking problem and the reference trajectory is obtained. Finally, model predictive control (MPC) is used to achieve the tracking of the target USV.

Results The simulation experimental results show that the tracking effect of the USV under the RTTP strategy is more stable with the root mean square error (RMSE) of the tracking distance decreased by 28.06% and the energy consumption reduced by 5.93%. It also has advantages in the smoothness of the control volume.

Conclusions Compared with the traditional RFTP strategy, the proposed RTTP strategy can effectively improve the stability of USV target tracking, giving it practical significance for the target tracking of USVs.