In this article, an iterative learning algorithm based on extended state observer (ESO) is proposed to deal with the propeller failure of an underwater vehicle. In this control scheme, the nonlinear feedback mechanism of ESO is transplanted to iterative learning processes; that is, the nonlinear function of the current output residual is used to adjust the value of the virtual fault in the next iteration. Additionally, to ensure the safety of the control torque, a saturated proportional-derivative (PD) controller is proposed. Finally, to achieve online parameter self-tuning, a fuzzy logic controller is employed in this control scheme to fuzzify the parameters of a saturated PD controller and ESO. The obtained results show the favorable speed of tracking convergence and the high precision of fault estimation.

High frequency surface wave radar (HFSWR) plays an important role in marine surveillance on account of its ability to provide wide-range early warning detection. However, vessel target track breakages are common in large-scale marine monitoring, which limits the continuous tracking ability of HFSWR. The following are the possible reasons for track fracture: highly maneuverable vessels, dense channels, target occlusion, strong clutter/interference, long sampling intervals, and low detection probabilities. To solve this problem, we propose a long-term continuous tracking method for multiple targets with stereoscopic HFSWR based on an interacting multiple model extended Kalman filter (IMMEKF) combined with an extreme learning machine (ELM). When the trajectory obtained by IMMEKF breaks, a new section of the track will start on the basis of the observation data. For multiple-target tracking, a number of broken tracks can be obtained by IMMEKF tracking. Then the ELM classifies the segments from the same vessel by extracting different features including average velocity, average curvature, ratio of the arc length to the chord length, and wavelet coefficient. Both the simulation and the field experiment results validate the method presented here, showing that this method can achieve long-term continuous tracking for multiple vessels, with an average correct track segment association rate of over 91.2%, which is better than the tracking performance of conventional algorithms, especially when the vessels are in dense channels and strong clutter/interference area.

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