In order to improve the performance of the global positioning system (GPS) and inertial nav-igation system (INS) integrated system during GPS outages, a novel fusion algorithm based on back propa-gation neural network (BPNN) is proposed. A new model is built which relates the INS velocity, inertial measurement unit (IMU) outputs and the duration of GPS outages to the GPS position increment. Perfor-mance of the proposed method has been experimentally evaluated in a land vehicle navigation test. The test results show: (1) the proposed model can efficiently predict the increment of position and compensate the INS errors accumulation during GPS outage; (2) the advantage of new model on positioning accuracy be-comes more obvious when the GPS observations are unavailable for a long time; (3) utilizing the current and past 2-step information as the input of BPNN model can effectively balance the computation burden and accuracy.

The installation error of USBL acoustic transceiver array is one of the important error sources for SINS/USBL integrated navigation system. The installation error of USBL transceiver array can be divided into coordinate shift error and angle rotation error. The angle rotation error (i.e. installation error angle) has a great influence on the positioning accuracy of USBL. In this paper, the traditional surround sailing method is used to study the relationship between the installation error angle and the USBL positioning accuracy. The influence on USBL positioning is analyzed from the component of heading, pitch and roll of installation error angle, and the simulation results show that the horizontal positioning errors of USBL are mainly affected by the heading and rolling installation errors. The horizontal positioning error of USBL caused by heading or rolling installation error of 1± will result in 17.45m, under the circumstance that the depth of transponder is 100m and the radius of surround sailing is 1000m, without considering the influence of other positioning errors. 1.75m horizontal positioning error of USBL caused by pitching positioning error of 1± pitching positioning error, and the horizontal positioning error of USBL caused by heading and pitching positioning error is also related to the horizontal distance between the transmitter and transponder. A dynamic calibration algorithm of installation error angle based on incremental iteration is proposed in this paper, which could realize the dynamic on-line estimation of installation error angle of USBL transceiver array. The installation error angle increment module can accurately estimate the installation angle error by iterating more than ten times. In the case of ignoring of the other error terms, the horizontal positioning error caused by the installation error angle can be reduced from 8 m to 2 m in the positioning range of 1000 m.

In this paper, a self-alignment method for strapdown inertial navigation systems based on the q-method is studied. In addition, an improved method based on integrating gravitational apparent motion to form apparent velocity is designed, which can reduce the random noises of the observation vectors. For further analysis, a novel self-alignment method using a Kalman filter based on adaptive filter technology is proposed, which transforms the self-alignment procedure into an attitude estimation using the observation vectors. In the proposed method, a linear psuedo-measurement equation is adopted by employing the transfer method between the quaternion and the observation vectors. Analysis and simulation indicate that the accuracy of the self-alignment is improved. Meanwhile, to improve the convergence rate of the proposed method, a new method based on parameter recognition and a reconstruction algorithm for apparent gravitation is devised, which can reduce the influence of the random noises of the observation vectors. Simulations and turntable tests are carried out, and the results indicate that the proposed method can acquire sound alignment results with lower standard variances, and can obtain higher alignment accuracy and a faster convergence rate.

In navigation of an autonomous underwater vehicles (AUVs), estimation of position is an important issue, especially when the sensors such as gyroscopes contain a lot of noise and the velocity information of Doppler Velocity Log (DVL) is affected by the motion attitude of the vehicle. In this paper, based on an improved auto regressive (AR) model, a real-time filter is utilized for Gyroscope signal de-noising. Meanwhile, according to the characteristics of the AUV, the influence of the vehicle attitude on the DVL velocity measurement error is analyzed and a motion attitude assist (MAA) method based on error model is introduced for enhancing DVL velocity accuracy. In this study, using the proposed hybrid approach, an inertial navigation system (INS)/DVL integrated navigation system is designed. The proposed approach is evaluated by simulation and experimental test in different acceleration bound and existence of DVL outage for an AUV. The results indicate that the precisions of the velocity and position are improved effectively, especially in complex motion attitude and long sailing conditions.

The ultrashort baseline system is widely used in ships and underwater navigation and positioning. The misalignment and level arm with the inertial measurement unit are the two sources of positioning inaccuracy. The accuracy of calibration is usually affected by measurement noise and linearization of the observation equation. In order to improve the calibration accuracy, the variational Bayesian unscented Kalman filter (VBUKF) method is proposed for the calibration of the ultrashort baseline installation error in the paper. The detailed derivation of VBUKF for the calibration is presented in the paper. Simulation experiments and field experiments were carried out, respectively, to verify the algorithm. The simulation results show that the proposed method can calibrate the installation error of the sensors in real time on line. The field experiment verified that the algorithm improves the calibration accuracy of the installation error under the large misalignment. The positioning accuracy is also improved compared with the traditional method.