This paper, in allusion to the limitations of traditional transfer alignment methods based on the external measurement equipment or the empirical model of angular deformation, proposes a rapid and accurate transfer alignment method without relying on the empirical angular deformation model. Firstly, the relationship between the actual angular deformation and the angular velocities measured by the gyroscopes in the master and slave inertial navigation systems (INSs) is derived to roughly estimate the angular deformation. Secondly, according to the error characteristics of gyroscopes, the error model of angular deformation is established. Thirdly, expanding the angular deformation error instead of the installation error angle, flexure angle and flexure angle rate into the state vector, a low-order transfer alignment filtering model independent of the empirical angular deformation model is established. The proposed method not only gets rid of the dependence on an empirical angular deformation model, but also realises the rapid and accurate initial alignment of the slave INS without adding any external measurement equipment. The simulations and experiments evidence the validity of the proposed transfer alignment method.

This paper presents a novel estimation method for coarse alignment of a marine strapdown inertial navigation system (SINS) under mooring conditions. The properties of gravitational motion are used to improve the accuracy of coarse alignment. The parametric motion of gravitational apparent is a circle that is on the surface of a sphere. The location of this parametric circle is dependent on the definition of the reference frames and the initial angles of SINS. In the method proposed here the initial direct cosine matrix is calculated only from the location of the gravity motion parametric circle. The novelty of this paper is to provide a new method for estimating the gravity motion trajectory in inertial frame, as well as direct extraction of the initial direct cosine matrix from this estimated trajectory. Simulation and testing show that the proposed method is suitable for coarse alignment in mooring conditions.

A new acoustic positioning method for Autonomous Underwater Vehicles (AUV) that uses a single underwater hydrophone is proposed in this paper to solve problems of Long Baseline (LBL) array laying and communication synchronisation problems among all hydrophones in the traditional method. The proposed system comprises a Strapdown Inertial Navigation System (SINS), a single hydrophone installed at the bottom of the AUV and a single underwater sound source that emits signals periodically. A matrix of several virtual hydrophones is formed with the movement of the AUV. In every virtual LBL window, the time difference from the transmitted sound source to each virtual hydrophone is obtained by means of a Smooth Coherent Transformation (SCOT) weighting cross-correlation in the frequency domain. Then, the recent location of the AUV can be calculated. Simulation results indicate that the proposed method can effectively compensate for the position error of SINS. Thus, the positioning accuracy can be confined to 2 m, and the method achieves good applicability. Compared with traditional underwater acoustic positioning systems, the proposed method can provide great convenience in engineering implementation and can reduce costs.

This paper proposes a dynamic aided inertial navigation method to improve the attitude accuracy for ocean vehicles. The proposed method includes a dynamic identification algorithm and the utilisation of dynamic constraints to derive additional observations. The derived additional observations are used to update the filters and limit the attitude error based on the dynamic knowledge. In this paper, two dynamic conditions, constant speed cruise and quasi-static, are identified and corresponding additional velocity and position observations are derived. Simulation and experimental results show that the proposed method can improve and guarantee the accuracy of the attitude. The method can be used as a backup method to bridge external information outages or unavailability. Both the features of independence of external support and integrity of the Inertial Navigation System (INS) are enhanced.

To meet the requirements of different applications, the International Global Navigation Satellite System (GNSS) Service (IGS) has provided Global Positioning System (GPS) satellite ephemerides and clock products with different accuracy levels (final, rapid and ultra-rapid products). Comparison of Precise Point Positioning/Inertial Navigation System/Ultra Wideband (PPP/INS/UWB) tightly coupled positioning with different precise satellite ephemeris and clock products is made and corresponding data analysis is provided. Final, rapid and ultra-rapid products are applied in a PPP/INS/UWB integrated system. The field trajectories, position and velocity errors of the integrated system with different products are compared. The results indicate that PPP/INS/UWB tightly coupled positioning with final and rapid products achieves an accurate performance. Compared with the position resolution using final products, the position accuracy with the ultra-rapid products (observed half) is slightly reduced and the position accuracy with the ultra-rapid products (predicted half) has a 0·1–0·2 m reduction. The influence of precise satellite ephemeris and clock products is very minor for non-real-time positioning, relative to the accuracy of PPP/INS/UWB tightly coupled positioning.

Tight integration of inertial sensors and stereo visual odometry to bridge Global Navigation Satellite System (GNSS) signal outages in challenging environments has drawn increasing attention. However, the details of how feature pixel coordinates from visual odometry can be directly used to limit the quick drift of inertial sensors in a tight integration implementation have rarely been provided in previous works. For instance, a key challenge in tight integration of inertial and stereo visual datasets is how to correct inertial sensor errors using the pixel measurements from visual odometry, however this has not been clearly demonstrated in existing literature. As a result, this would also affect the proper implementation of the integration algorithms and their performance assessment. This work develops and implements the tight integration of an Inertial Measurement Unit (IMU) and stereo cameras in a local-level frame. The results of the integrated solutions are also provided and analysed. Land vehicle testing results show that not only the position accuracy is improved, but also better azimuth and velocity estimation can be achieved, when compared to stand-alone INS or stereo visual odometry solutions.

A new integration of the acquisition and tracking modes is proposed for the integration of a Celestial Navigation System (CNS) and a Strapdown Inertial Navigation System (SINS). After the integration converges in the acquisition mode, it switches to the tracking mode. In the tracking mode, star pattern recognition is unnecessary and the integration is implemented in a cascaded filter scheme. A pre-filter is designed for each identified star and the output of the pre-filter is fused with the attitude of the SINS in the cascaded navigation filter. Both the pre-filter and the navigation filter are designed in detail. The measurements of the pre-filter are the positions on the image plane of one identified star. Both the starlight direction and its error are estimated in the pre-filter. The estimated starlight directions of all identified stars are the measurements of the navigation filter. The simulation results show that both the reliability and accuracy of the integration are improved and the integration is effective when only one star is identified in a period.

This paper presents a reliable in-motion alignment algorithm for a low cost Strapdown Inertial Navigation System/Global Positioning System (SINS/GPS) combination under random misalignment angles, which transforms attitude alignment into an attitude estimation problem. Based on Rodrigues parameters, an alignment model with a linear state-space equation and a second order nonlinear measurement equation is established. Furthermore, by employing a Taylor expansion on the nonlinear measurement equation, we implement a second order Extended Kalman Filter (EKF2). The proposed method uses a single filter that can not only determine the initial attitude, but also estimate the sensor errors. In addition, a scheme is given for avoiding singularity, which makes the algorithm more widely suitable for random misalignment angles. Experimental ground tests are performed with a low-cost Micro-Electromechanical System (MEMS) SINS, which validates the efficacy of the proposed method. The performance compared to the traditional alignment algorithm is also given.

This paper proposes a novel sensor fusion approach using Ultra Wide Band (UWB) wireless radio and an Inertial Navigation System (INS), which aims to reduce the accumulated error of low-cost Micro-Electromechanical Systems (MEMS) Inertial Navigation Systems used for real-time navigation and tracking of mobile robots in a closed environment. A tightly-coupled model of INS/UWB is established within the integrated positioning system. A two-dimensional kinematic model of the mobile robot based on kinematics analysis is then established, and an Auto-Regressive (AR) algorithm is used to establish third-order error equations of the gyroscope and the accelerometer. An Improved Adaptive Kalman Filter (IAKF) algorithm is proposed. The orthogonality judgment method of innovation is used to identify the “outliers”, and a covariance matching technique is introduced to judge the filter state. The simulation results show that the IAKF algorithm has a higher positioning accuracy than the KF algorithm and the UWB system. Finally, static and dynamic experiments are performed using an indoor experimental platform. The results show that the INS/UWB integrated navigation system can achieve a positioning accuracy of within 0·24 m, which meets the requirements for practical conditions and is superior to other independent subsystems.

This paper presents a framework for a strapdown Inertial Navigation System (INS) algorithm design by using Lie group and Lie algebra. The general way to solve Lie group differential equations is introduced. Investigations reveal that this general Lie group method provides a simpler unified way to solve differential equations involving direction cosine matrix, quaternion and dual quaternion, which are widely used in INS algorithm design. Furthermore, we also present a new INS algorithm based on the Special Euclidean group se(3) under the guidelines of Lie group method. Analyses show that se(3) algorithm has the same accuracy as a dual quaternion algorithm, this is also justified by numerical simulations. Though the se(3) algorithm has no improvements in accuracy, the general Lie group method used in the design process shows its brevity and uniformity.

Autonomous Unmanned Ground Vehicles (UGVs) require a reliable navigation system that works in all environments. However, indoor navigation remains a challenge because the existing satellite-based navigation systems such as the Global Positioning System (GPS) are mostly unavailable indoors. In this paper, a tightly-coupled integrated navigation system that integrates two dimensional (2D) Light Detection and Ranging (LiDAR), Inertial Navigation System (INS), and odometry is introduced. An efficient LiDAR-based line features detection/tracking algorithm is proposed to estimate the relative changes in orientation and displacement of the vehicle. Furthermore, an error model of INS/odometry system is derived. LiDAR-estimated orientation/position changes are fused by an Extended Kalman Filter (EKF) with those predicted by INS/odometry using the developed error model. Errors estimated by EKF are used to correct the position and orientation of the vehicle and to compensate for sensor errors. The proposed system is verified through simulation and real experiment on an UGV equipped with LiDAR, MEMS-based IMU, and encoder. Both simulation and experimental results showed that sensor errors are accurately estimated and the drifts of INS are significantly reduced leading to navigation performance of sub-metre accuracy.

This paper presents a new type of Global Positioning System/Inertial Navigation System (GPS/INS) providing higher navigation accuracy under large initial heading error. The mechanization introduced is applicable to low cost GPS/INS systems and enhances the performance when the heading error is large. The proposed approach has the capability to decrease large heading errors very quickly and can start the strapdown navigation computations under poor heading accuracy without any special alignment procedure. Although the design is applicable to land, sea and aerial vehicles, a land vehicle is used for the performance tests. The test is conducted around a closed path and the proposed system is compared to a GPS/INS system based on small attitude error assumption. The performance of both systems is given in this paper.

Positioning and navigation data for unmanned surface vehicles (USVs) are extracted using the Global Positioning System (GPS) and the Inertial Navigation System (INS) integrated with an inertial measurement unit (IMU). The integration of quaternion with direction cosine matrix (DCM) with the aim of obtaining high accuracy with complete system independence has been effectively used to supply position and attitude information for autonomous navigation of marine crafts. A DCM integrated with a quaternion provided an advanced technique for precise USV attitude estimation and position determination using low-cost sensors. This paper presents the implementation of an INS developed by the integration of DCM and quaternion.