The self-alignment process for the strapdown inertial navigation system (SINS) is performed mainly to achieve an accurate initial attitude only using the measurements from the inertial measurement unit (IMU). Different from conventional self-alignment methods, a gradient descent optimization-based SINS self-alignment method, which can determine the initial attitude without using the latitude information, is proposed in this paper. According to certain geometry constraints of the earth rate vector in the navigation frame, we use the measurements from the IMU to represent the earth rate vector instead of directly using the latitude information. To overcome the sensor noise disturbance, we also construct a quaternion-based nonlinear objective function with the estimated earth rate vector, which is formulated as seeking the least square solution of a Wahba problem. Thus, we employ the gradient descent optimization to achieve the optimal solution of the nonlinear objective function. The simulation and experiment results verify the alignment performance and flexibility of the proposed self-alignment method for SINS stationary alignment without using a priori local latitude information.

To solve the problem of unknown state noises and uncertain measurement noises inherent in underwater cooperative navigation, a new Variational Bayesian (VB)-based Adaptive Extended Kalman Filter (VBAEKF) for master–slave Autonomous Underwater Vehicles (AUV) is proposed in this paper. The Inverse Wishart (IW) distribution is used to model the predicted error covariance and measurement noise covariance matrix. The state, together with the predicted error covariance and measurement noise covariance matrix, can be adaptively estimated based on VB approximation. The performance of the proposed algorithm is demonstrated through a lake trial, which shows the advantage of the proposed algorithm.

In the leader–follower-based multiple underwater unmanned vehicles (UUVs) cooperative positioning system, the precision of the compass and Doppler velocity log equipped with the follower UUV is low, which will inevitably lead to yaw error and velocity error (YEaVE) in the output data. These errors will seriously affect the positioning accuracy of follower UUV. In addition, the observation information often has many outliers due to the influence of underwater multipath, which will cause the increase of the positioning error. Herein, we propose a novel adaptive sliding observation-based cooperative positioning algorithm under the factor graph framework. First, the influence mechanism of YEaVE on positioning accuracy of the follower UUV is analyzed. Thereafter, under the factor graph framework, a cooperative positioning algorithm based on maximum correntropy criterion is designed in which the estimation and compensation of YEaVE are innovatively added. At last, an adaptive sliding observation method is designed to deal with outliers in observation information. The proposed algorithm is verified by simulations and shipboard experiment, and the results show that it can accurately estimate the YEaVE. At the same time, compared with the ordinary factor graph algorithm, the positioning accuracy of the proposed algorithm has been improved by 88.31% and 81.63% in simulation and shipboard experiment.

Along with the development of computer technology and informatization, the unmanned vehicle has become an important equipment in military, civil and some other fields. The navigation system is the basis and core of realizing the autonomous control and completing the task for unmanned vehicles, and the Strapdown Inertial Navigation System (SINS) is the preferred due to its autonomy and independence. The initial alignment technique is the premise and the foundation of the SINS, whose performance is susceptible to system nonlinearity and uncertainty. To improving system performance for SINS, an improved initial alignment algorithm is proposed in this manuscript. In the procedure of this presented initial alignment algorithm, the original signal of inertial sensors is denoised by utilizing the improved signal denoising method based on the Empirical Mode Decomposition (EMD) and the Extreme Learning Machine (ELM) firstly to suppress the high-frequency noise on coarse alignment. Afterwards, the accuracy and reliability of initial alignment is further enhanced by utilizing an improved Robust Huber Cubarure Kalman Filer (RHCKF) method to minimize the influence of system nonlinearity and uncertainty on the fine alignment. In addition, real tests are used to verify the availability and superiority of this proposed initial alignment algorithm.

The implementation principle of a typical three-pulse cold atom interference gyroscope is introduced in this paper. Based on its configuration and current research status, the problems of cold atom interference gyro are pointed out. The data-rate is insufficient, and it is difficult to achieve high dynamic measurement. Then, based on these two limitations, a novel design of the monitoring navigation system of the cold atom interference gyroscope (CAIG) and an intermediate-grade inertial measurement unit (IMU) was proposed to obtain the long-term position result without GPS signals, such as the Inertial Navigation System (INS) in underwater vehicles. While the CAIG was used as the external gyro, the bias of IMU and the misalignment angle between the CAIG-frame and the IMU-frame are obtained through filtering technique. The simulation test and field test demonstrated the improvements of the long-term positioning accuracy of the INS.

Relative positioning among vehicles finds many applications regarding location-based services in intelligent transportation systems. This paper introduces a hybrid relative positioning strategy integrating GNSS, INS and ultra-wideband (UWB) observations, which addresses measurement outliers and GNSS outages simultaneously. An improved extended Kalman filter (IEKF) based on tracking an appropriately weighted window of past innovations is developed to deal with measurement outliers. To perform relative positioning during GNSS outages, a nonlinear autoregressive network with exogenous inputs (NARX) is developed to predict GNSS measurements increments, with the least-squares support vector machine (LSSVM) algorithm identifying the NARX model. The NARX-LSSVM module learns the relationship between GNSS pseudorange and Doppler shift increments of a target vehicle and its local and neighbors’ dynamics. Whereas during GNSS outages, the increments of GNSS measurements are generated by this prediction algorithm. The feasibility of the proposed methodology is evaluated on empirical road data, and improved positioning accuracy is achieved in cooperative positioning systems.