MEMS-IMU Based Nonlinear Filtering Methods Research For The Navigation System Of USV

As a most vitial component of Unmanned Surface Vehicle(USV),the navigation system is a guarantee for the USV to efficiently accomplishdifferenttasks on their own saftly in various complex sea conditions.The navigation system of the USV has to balance cost,size,mass,power and accuracy for its low development costs and limited capability of payloads.Micro-electromechanical systemInertial measurement Units(MEMS-IMU)are marked by micro-weighte,low power consumption and cost-effective,but low accuracy at the same time.The research of INS/GNSS filtering algorithm based onMEMS-IMU plays an important role in USVs with low-cost,low-load,and high accuracy requirements.For the state estimation based on MEMS-IMU of USV navigation syste,there are following problems need to be solved: Firstly,the low measurement accuracy intensify the nonlinearity of INS/GNSS integrated navigation,more powerful nonlinear state estimation algorithm is needed to solve such problem;Secondly,the statistic characteristics of the GNSS measurements have contaminated disstribution or outliers,and cannot be accurately obtained,an online statistic characteristics estimation algorithm for GNSS measurements is needed;Thirdly,commonly used methods for state estimation of navigation system are Bayesian theory based and specially tailored variants.While the Bayesian methods has found wide applicability,it does not come without some drawbacks.This includes relatively high computational cost and a rather implicit and not easily verifiable convergence properties.Based on the analysis above,the main contents of this paper are as follows:As to the nonlinear non-Gaussian characteristic of the INS/GNSS tightly coupled system,a framework of Bayes estimation theory based hybrid particle filter algorithm is proposed.This algorithm first obtains posterior state estimates based on Bayes filtering,then regard these distribution as importance density function for generating particles.As theparticles are not reused,it will avoidthe problems such as particle degeneration and re-sampling.The algorithm guarantees high estimation accuracy and less particles.The covariance of measurement noise has inaccurate statistical properties,and may has sudden change,an adapting unscented particle filtering algorithm based on residual sequence is proposed.By simulation,we proved that when the variance of measurements noise of pseudorange and pseudorange rate have sudden changes,AUPF algorithm may track and capture the trend of the noise variance,thus obtaining higher filtering accuracy.When the measurement noise covariance of GNSS is unknown,time-varying,disturbed or there are outliers in the measurements,variational Bayesian robust adaptive unscented particle filter algorithm based on variational Bayesian estimation theory and Huber robust estimation theory is proposed.By using inverse Wishart distribution to approximate the covariance distribution of the measurement noise,Gaussian distribution assumation-based variational Bayesian adaptive unscented Kalman filtering algorithm(VBAUKF)is proposed.Consider that GNSS measurement may be disturbed by outliers,we propose VBRAUKF algorithm by combining VBAUKF algorithm with Huber estimation.Then by combining the Hybrid particle filter algorithm framework proposed before,the algorithm VBRAUPF is derived.Simulation results show that,when the statistical property of the measurement noise is time-varying,or the noise is disturbed or there are outliers in the measurements,VBRAUPF can efficiently solve the problem of filtering divergence in INS/GNSS tightly coupled integration systems,enhancing the robustness of the system.As to the multi-sensor INS/GNSS/Magnetometer integrated navigation state estimation problem,aiding virtual vertical measurement(AVVM)based nonlinear observer is proposed.First transfer the state estimation of multi-sensor integration problem into the problem of state estimation in nonlinear feedback-interconnectedsystem.Then calculate observer gain matrix with Riccati equation based on nonlinear observer theory.The stability of the observer can be analyzed through Lyapunov theory.The simulation results show that the attitude observer can provide both quaternion estimation as well as the bias estimation of the gyroscope.By introducing AVVM,the estimation accuracy of vertical position and speed of a USV can be improved.When a USV sails at a low speed,the first order wave force may cause the ware down of the actuators.In order to solve this,this thesis proposed an adaptive wave filter observer which is not based on the dynamic model of a ship.First derive estimation algorithm for the wave encounter frequency,then design an adaptive notch filter based on the estimation.The output of the notch filter,accompanied with the output of nonlinear observer of the navigation system,can then be used to reconstruct the states of low frequency in the horizontal plane.