Sensor fusion for heading angle estimation based on random forest algorithm

To improve the accuracy of the calculation of a heading angle under magnetic interference,magnetometers and inertial measurement units(IMUs)were fused.The observation value of the heading angle was deduced on the basis of the modeling of the magnetometer error and the analysis of the relation of the magnetometer triaxial output and the distribution characteristics of the magnetic field at two adjacent time periods.Meanwhile,the gyro state and angular velocity increment were used as the basis of the IMU to calculate the prediction value of the heading angle.With the changes in the heading angle and environmental interference,a random forest(RF)algorithm was used to iteratively calculate the weights to fuse the observation value of the heading angle based on the magnetometer and the prediction value of the heading angle based on the IMU.The results show that relative to the common sensor fusion method,the proposed sensor fusion method based on the RF algorithm achieved an approximate 60%improvement in heading angle accuracy.Hence,the proposed method can effectively improve the accuracy of the heading angle under magnetic interference by using an RF algorithm to calculate the output weights of the magnetometer and IMU.

Theory and practice of hull dynamic deformation law and heading angle transfer error correction

In order to meet the demand of high-precision heading angle transmission in the transfer alignment of inertial navigation system on moving base,the analytical function relationship between the hull deforma-tion and the turning angular velocity and angular acceleration was derived by using the classical beam theory based on the analysis of the equivalent load exerted by the hydrodynamic force and inertia force on the hull structure during the turning process under the combined action of the steering rudder mo-ment and wave force.The objective law between the angular motion and the azimuth deformation angle of the hull under the combined action of maneuvering and sea waves was revealed.Finally,the correc-tion coefficients were determined according to the left turn and right turn motions of the hull by using the measured data of the ship in the sea trial during the S-shape maneuvering navigation,and the az-imuth deformation angle correction was completed.The results indicated that the application of the Qu’s bending deformation correction formula could greatly reduce the influence of the hull flexural deforma-tion on the heading angle accuracy,meet the needs of high-precision heading angle transmission,and fully verify the correctness of the hull azimuth deformation law and the heading angle transmission er-ror correction theory.This theory and method provided technical support for establishing high-precision distributed digital reference in the field of transfer alignment of inertial navigation on moving base and the application of heading angle transfer of other shipborne equipment.

Robust UKF algorithm in SINS initial alignment

In the traditional unscented Kalman filter（UKF）,accuracy and robustness decline when uncertain disturbances exist in the practical system.To deal with the problem,a robust UKF algorithm based on an H-infinity norm is proposed.In Krein space,a robust element is added in the simplified UKF so as to improve the algorithm.The filtering gain is adjusted by the robust element and in this way the performance of the robustness of the filtering algorithm is promoted.In the initial alignment process of the large heading misalignment angle of the strapdown inertial navigation system（SINS）,comparative studies are conducted on the robust UKF and the simplified UKF.The simulation results illustrate that compared with the simplified UKF,the robust UKF is more accurate,and the estimation error of heading misalignment decreases from 16.9＇ to 4.3＇.In short,the robust UKF can reduce the sensitivity to the system disturbances resulting in better performance.

Attitude Head Pursuit Transition Guidance Law

As an improved guidance method,the attitude head pursuit guidance （AHPG） law enables the attitude pursuit guidance （APG） law to be more suited to transition guidance of air-to-ground missiles. By adding a head angle into the attitude angle of APG,AHPG directs the missile axis onto the line of sight （LOS）. The maximum range trajectory simulation shows that the elevator deflection angle reaches the saturated value of 10° at the outset and the impact angle is less than 60° when APG is used as transi-tion guidance law. However,the elevator deflection angle on the whole trajectory is reduced to under 5° and the impact angle increased to over 60° when AHPG is used. The formulae to calculate head angles are derived for different target distributions. The simulation of multiple trajectories shows that with the help of the formulae based on AHPG law,the same performance could be achieved.

Development of a low-cost GPS/INS integrated system for tractor automatic navigation

The use of low-cost single GPS receivers and inertial sensors for auto-guidance applications has been limited by their reduced accuracy and signal drift over time compared to real-time kinematic(RTK)differential GPS units and fiber-optic gyroscope(FOG)sensors.In this study,a prototype low-cost GPS/INS integrated system consisting of a triangle-shaped array of three Garmin 19x GPS receivers and an Xsens inertial measurement unit(IMU)to improve the accuracy of position and heading angle measured with a single GPS receiver was developed.A triangular algorithm that uses data collected from the three single GPSs mounted on the angular points of a triangular frame was designed.A sensor fusion algorithm based on the Kalman filter combining the GPS and IMU data was developed by integrating position data and heading angles of a triangular array of GPS receivers.The optimized values of two noise covariance matrixes(Q and R)for the Kalman filtering were determined using the Central Composite Design(CCD)method.As compared to the use of a single Garmin GPS receiver,use of the developed GPS/INS system showed improved accuracy performance in terms of both position and heading angle,with reductions in root mean square errors(RMSEs)from 2.7 m to 0.64 m for position and from 8.9ºto 2.1ºfor heading angle.The accuracy improvements show new potential for agricultural auto-guidance applications.