Research On SINS/DVL Integrated Navigation Algorithm

The exploration and development of marine resources is an important topic in the development of human society.Autonomous underwater vehicles have become an important carrier for the exploration and development of the ocean,and should meet the technical requirements of high-precision navigation in the long voyage.Therefore,the accuracy of underwater integrated navigation is an important index to evaluate autonomous underwater vehicles.In order to improve the ability of autonomous navigation,this paper studies the SINS / DVL integrated navigation algorithm.The algorithm is mainly divided into two parts,inertial navigation and Doppler log installation deviation calibration algorithm and SINS / DVL combination algorithm.In order to improve the speed accuracy of DVL in integrated navigation,it is necessary to calibrate the installation deviation between ins and Doppler log.In this paper,four kinds of speed based calibration methods are analyzed theoretically,calibration models are established and verified by simulation experiments.The calibration factor,installation deviation angle and lever error can not be calibrated at the same time.The classification calibration method is used to calibrate them one by one,and the Kalman filter algorithm of installation deviation angle is used to analyze the observability.Under the condition that sins and DVL are connected with the ship,the heading angle and pitch angle are observable,and the roll angle is not observable.The principle of dual vector and multi vector attitude determination is analyzed,and there is no theoretical error,but the dual vector should be In order to avoid the linearization error of attitude matrix,the least square calibration model and the Kalman filter calibration model are established.The position based calibration method is analyzed,which is more simple and practical.The SINS / DVL integrated navigation algorithm model is established,including the state equation and the measurement equation.The observability of the state quantity of the integrated navigation algorithm is analyzed,and the observability of the speed,pitch angle and roll angle is strong.The simulation results show that the estimation quantity of the feedback speed,pitch angle and roll angle is higher than that of the single feedback speed.DVL has velocity outliers in continuous operation,which affects the accuracy of integrated navigation.In this paper,two methods of outliers elimination based on covariance idea and outliers elimination based on innovation matching are used to carry out simulation experiments to verify that the velocity outliers of DVL in integrated navigation are effectively eliminated.In order to further improve the accuracy of integrated navigation,sage Husa adaptive filter for measurement noise is adopted,and the adaptive algorithm is analyzed theoretically.Aiming at the shortcomings of adaptive filter,an improved adaptive filter method is proposed,and a scale factor is introduced to weight the measurement noise estimation corresponding to the measurement innovation according to the fluctuation of the measurement innovation.Simulation experiments are carried out to make it adaptive The improved adaptive algorithm is better than the adaptive algorithm.Aiming at the problem of new information mismatch in the adaptive algorithm,the strong tracking algorithm is introduced.By adjusting the covariance matrix of state prediction,the integrated navigation algorithm can track the change of measurement value better.The simulation experiment shows that the strong tracking algorithm can solve the problem of new information mismatch.The SINS / DVL integrated navigation lake experiment is carried out,and the real data are processed and analyzed.The position calibration is superior to the speed calibration,and the position calibration results are used to process the integrated navigation data.The observable measures of the state variables of the integrated navigation are obtained with the lake test data,and the feedback pitch roll estimator is effective.The outliers are eliminated based on the covariance idea and the outliers based on the innovation matching Both methods can effectively eliminate outliers;adaptive algorithm is better than Kalman filter,and improved adaptive algorithm is better than adaptive algorithm;strong tracking algorithm solves the problem of information mismatch in real environment.