Research On State Estimation And Control Method Of Target Tracking Of Sea Surface Rescue

The submarine will float on the sea suffered the environmental factors such as the wind,wave and current due to the accident loss of power.In order to ensure the safety of the submarine personnel and property,it is important to the timely rescue.The rescue ship detectes the target submarine in real time through the sensor,and close to the target,tracks the target and keeps the relative distance and pose with the target submarine,and then carries out the effective rescue operation.It is quite difficult and dangerous to carry out the rescue operation in the four level sea conditions because of the transient loss of the sensor.Therefore,it is practical to study the automatic rescue control system.In this paper,aiming at the target rescue and tracking control system of the sea surface,the following research work is mainly carried out:Firstly,the dynamic model of the rescue ship and target submarine is established,and the environmental factors such as wind,wave and current are modeled.The rationality and correctness of the models are verified by simulation and experiments.Secondly,the relative distance and heading calculation scheme are designed based on the measurement principle of the MiniRadascan microwave reference system.The Cubature particle filter is designed to estimate the target state under the condition of non-Gaussian noise because of the poor performance of conventional Gaussian filter.Aiming at the problems of large computation and poor real time performance in the Cubature particle filter,The Gaussian mixture cubature kalman filter is proposed to improve the accuracy and real time performance of target state estimation.Thirdly,a six degree of freedom swing platform fixed with MiniRadascan is used to simulate the loss of observations.The state transition model based on the historical observation data fit prediction is designed because that the existing state estimation method can only use the system model prediction while the observation missing,and an observation data sliding window width adaptive adjustment strategy is proposed,which can improve the fitting accuracy of the state transition model.In order to solve the problem that the weight value of the Gaussian components of the Gaussian mixture filter are not changed in the time update stage while the observation missing,An adaptive adjustment strategy for the wight is proposed based on the Chapman-Kolmogorov,which can improve the estimation precision under the conditions of observation missing.Then,in order to solve the problem that the unnecessary loss of the control system of the rescue ship caused by the high frequency motion,the motion observer of the rescue ship is designed.A constant bearing tracking control guidance law is proposed to stabilize the input of the tracking controller.A filter backstepping controller based on the observer is designed to solve the problem of multiple analytic derivation for the virtual control variable by the conventional backstepping controller.The particle swarm optimization algorithm is used for parameter optimization of the controller to improve the tracking precision.At the same time,the anti-windup is added to realize the stable output of the controller,and the effectiveness of the proposed controller is verified by the simulation experiment.Finally,a target tracking simulation test system is built basesd on the six degree of freedom motion platform.The Gaussian mixture cubature Kalman filter and the state estimation algorithm under the condition of observation missing are verified by the measured data of MiniRadascan.The test result proved the feasibility and the accuracy of the proposed state estimation method and the tracking controller.