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IN covering British activities in the inertial guidance field, it is appropriate to include a brief picture of developments in the historical sense so that British achievements can be seen in perspective especially with regard to the state‐of‐the‐art in the United States. We will also deal with the application of inertial navigation systems to the civil field for, although at the moment every production inertial quality system in service is being operated in a military rôle, civil aviation is on the threshold of adopting inertial techniques.

The purpose of this paper is to develop an integrity monitoring method using ERAIM (Extended Receiver Autonomous Integrity Monitoring) for the integrated GNSS/Inertial (Global Navigation Satellite System and inertial navigation system) of general aviation aircraft.

First the tightly integrated GNSS with Strapdown Inertial Navigation System (GNSS/SINS) and the Kalman filter is designed. Then the processing of ERAIM is presented, in which the least‐squares theory is used to calculate the best estimators by integrating the predicted states with measurement states of Kalman filter. Based on the new measurement model, the integrity monitoring for GNSS/inertial system is carried out, including the fault detection, identification, reliability and separability. Lastly, the simulation and analysis for ERAIM vs RAIM are performed to validate the proposed method.

Simulation results show that the ERAIM method is able to detect and identify effectively any type of failure including step failure and ramp failure. Compared to the RAIM method for only GNSS, the ERAIM increases the redundant information and reduces the correlation of test statistics, as well as enhancing the reliability and thus can significantly improve the performance of integrity monitoring.

In safety critical sectors such as aviation, stringent integrity performance requirements must be met. The ERAIM method cannot only be used in integrity monitoring for the integrated GNSS/Inertial system, but also can be applied to only GNSS or other integrated navigation systems for general aviation aircraft.

The paper presents a new integrity monitoring method of ERAIM, which is able to improve the fault detection and identification capabilities significantly by extending GNSS‐used RAIM method into the GNSS/Inertial integrated system.

THE simplest concept of inertial navigation is that of using an accelerometcr for measurement of vehicle acceleration, integrating once to give a measure of velocity and twice to give a measure of distance travelled (fig. 1). For this purpose, the direction of the input axis of the accelerometcr must be kept constant, or alternatively its direction at all times must be known and its changing direction must be taken into account in interpreting its output. In either case, provision of a stable attitude reference is necessary. Gyroscopes can provide this feature.

The purpose of this paper is to overcome the limitations of existing celestial horizon references, and improve the navigation accuracy of the strap‐down inertial navigation system/celestial navigation system (SINS/CNS) integrated system with an innovative scheme of deep integration.

First, a novel conception of mathematical horizon reference (MHR) provided by the strap‐down matrix of SINS is proposed. Then, the realization mechanism of the MHR‐based vertical vector is introduced from the viewpoint of vector rotation. Moreover, the MHR implementation scheme of high precision and reliability is presented, and on this basis, the method which utilizes vertical vector to achieve celestial navigation is introduced. In addition, with considering the characteristics of SINS and the MHR‐based CNS, the SINS/CNS deep integrated navigation system and its specific realization are proposed. Finally, simulation tests are implemented to validate this SINS/CNS deep integrated navigation method.

The innovative SINS/CNS deep integrated system could make full use of SINS and CNS navigation information to achieve higher navigation accuracy for the long‐duration and high‐altitude vehicles.

This paper provides a novel realization method of high precision MHR and the MHR‐based SINS/CNS deep integration.

This study aims to design an optimized algorithm for low-cost pedestrian navigation system (PNS) to correct the heading drift and altitude error, thus achieving high-precise pedestrian location in both two-dimensional (2-D) and three-dimensional (3-D) space.

A novel heading correction algorithm based on smoothing filter at the terminal of zero velocity interval (ZVI) is proposed in the paper. This algorithm adopts the magnetic sensor to calculate all the heading angles in the ZVI and then applies a smoothing filter to obtain the optimal heading angle. Furthermore, heading correction is executed at the terminal moment of ZVI. Meanwhile, an altitude correction algorithm based on step height constraint is proposed to suppress the altitude channel divergence of strapdown inertial navigation system by using the step height as the measurement of the Kalman filter.

The verification experiments were carried out in 2-D and 3-D space to evaluate the performance of the proposed pedestrian navigation algorithm. The results show that the heading drift and altitude error were well corrected. Meanwhile, the path calculated by the novel algorithm has a higher match degree with the reference trajectory, and the positioning errors of the 2-D and 3-D trajectories are both less than 0.5 per cent.

Besides zero velocity update, another two problems, namely, heading drift and altitude error in the PNS, are solved, which ensures the high positioning precision of pedestrian in indoor and outdoor environments.

The study aims to propose reverse processing solution to improve the performance of strapdown inertial navigation system (SINS) initial alignment and SINS-/global positioning system- (GPS) integrated navigation. The proposed scheme can be well applied in the fields of aircraft and aerospace navigation.

For the SINS alignment phase, a fast initial alignment scheme is proposed: the initial value of reverse filter is determined by the final result of forward filter, and then, the reverse filter is carried out using the stored data. Multiple iterations are performed until the accuracy is satisfied. For the SINS-/GPS-integrated phase, a forward–reverse navigation algorithm is proposed: first, the standard forward filter is used, and then, the reverse filter is carried out using the initial value determined by the forward filter, and the final fusion results are achieved by the weighted smoothing of the forward and reverse filtering results.

The simulation and the actual test results show that in the initial alignment stage, the proposed reverse processing method can obviously shorten the SINS alignment time and improve the alignment accuracy. In the SINS-/GPS-integrated navigation data fusion stage, the proposed forward–reverse data fusion processing can, obviously, improve the performance of the navigation solution.

The proposed reverse processing technology has an important application in improving the accuracy of navigation and evaluating the performance of real-time navigation. The proposed scheme can be not only used for SINS-/GPS-integrated system but also applied to other integrated systems for general aviation aircraft.

Compared with the common forward filtering algorithm, the proposed reverse scheme can not only shorten alignment time and improve alignment accuracy but also improve the performance of the integrated navigation.

There are shortcomings of modern methods of ensuring the stability of Kalman filtration in unmanned vehicles’ (UVs) navigation systems under the condition of a priori uncertainty of the dispersion matrix of measurement interference. First, it is the absence of strict criteria for the selection of adaptation coefficients in the calculation of the a posteriori covariance matrix. Secondly, it is the impossibility of adaptive estimation in real time from the condition of minimum covariance of the updating sequence due to the necessity of its preliminary calculation.

This paper considers a new approach to the construction of the Kalman filter adaptation algorithm. The algorithm implements the possibility of obtaining an accurate adaptive estimation of navigation parameters for integrated UVs inertial-satellite navigation systems, using the correction of non-periodic and unstable inertial estimates by high-precision satellite measurements. The problem of adaptive estimation of the noise dispersion matrix of the meter in the Kalman filter can be solved analytically using matrix methods of linear algebra. A numerical example illustrates the effectiveness of the procedure for estimating the state vector of the UVs’ navigation systems.

Adaptive estimation errors are sharply reduced in comparison with the traditional scheme to the range from 2 to 7 m in latitude and from 1.5 to 4 m in longitude.

The simplicity and accuracy of the proposed algorithm provide the possibility of its effective application to the widest class of UVs’ navigation systems.

The purpose of this paper is to relate to the real-time navigation and tracking of pedestrians in a closed environment. To restrain accumulated error of low-cost microelectromechanical system inertial navigation system and adapt to the real-time navigation of pedestrians at different speeds, the authors proposed an improved inertial navigation system (INS)/pedestrian dead reckoning (PDR)/ultra wideband (UWB) integrated positioning method for indoor foot-mounted pedestrians.

This paper proposes a self-adaptive integrated positioning algorithm that can recognize multi-gait and realize a high accurate pedestrian multi-gait indoor positioning. First, the corresponding gait method is used to detect different gaits of pedestrians at different velocities; second, the INS/PDR/UWB integrated system is used to get the positioning information. Thus, the INS/UWB integrated system is used when the pedestrian moves at normal speed; the PDR/UWB integrated system is used when the pedestrian moves at rapid speed. Finally, the adaptive Kalman filter correction method is adopted to modify system errors and improve the positioning performance of integrated system.

The algorithm presented in this paper improves performance of indoor pedestrian integrated positioning system from three aspects: in the view of different pedestrian gaits at different speeds, the zero velocity detection and stride frequency detection are adopted on the integrated positioning system. Further, the accuracy of inertial positioning systems can be improved; the attitude fusion filter is used to obtain the optimal quaternion and improve the accuracy of INS positioning system and PDR positioning system; because of the errors of adaptive integrated positioning system, the adaptive filter is proposed to correct errors and improve integrated positioning accuracy and stability. The adaptive filtering algorithm can effectively restrain the divergence problem caused by outliers. Compared to the KF algorithm, AKF algorithm can better improve the fault tolerance and precision of integrated positioning system.

The INS/PDR/UWB integrated system is built to track pedestrian position and attitude. Finally, an adaptive Kalman filter is used to improve the accuracy and stability of integrated positioning system.

The purpose of this paper is to propose a seamless autonomous navigation method based on the motion constraint of the mobile robot, which is able to meet the practical need of maintaining the navigation accuracy during global positioning system (GPS) outages.

The seamless method uses the motion constraint of the mobile robot to establish the filter model of the system, in which the virtual observation about the speed is used to overcome the shortage of the navigation accuracy during GPS outages. The corresponding motion constraint model of the mobile robot is established. The proposed seamless navigation scheme includes two parts: the micro inertial navigation system (MINS)/GPS-integrated filter model and the motion constraint filter model. When the satellite signals are good, the system works on the MINS/GPS-integrated mode. If some obstacles block the GPS signals, the motion constraint measurement equation will be effective so as to improve the navigation accuracy of the mobile robot.

Three different vehicle tests of the mobile robot show that the seamless navigation method can overcome the shortage of the navigation accuracy during GPS outages, so as to improve the navigation performance in practical applications.

A seamless navigation system based on the motion constraint of the mobile robot is proposed to overcome the shortage of the navigation accuracy during GPS outages, thus improving the adaptability of the robot navigation.

The purpose of this study is to use visual and inertial sensors to achieve real-time location. How to provide an accurate location has become a popular research topic in the field of indoor navigation. Although the complementarity of vision and inertia has been widely applied in indoor navigation, many problems remain, such as inertial sensor deviation calibration, unsynchronized visual and inertial data acquisition and large amount of stored data.

First, this study demonstrates that the vanishing point (VP) evaluation function improves the precision of extraction, and the nearest ground corner point (NGCP) of the adjacent frame is estimated by pre-integrating the inertial sensor. The Sequential Similarity Detection Algorithm (SSDA) and Random Sample Consensus (RANSAC) algorithms are adopted to accurately match the adjacent NGCP in the estimated region of interest. Second, the model of visual pose is established by using the parameters of the camera itself, VP and NGCP. The model of inertial pose is established by pre-integrating. Third, location is calculated by fusing the model of vision and inertia.

In this paper, a novel method is proposed to fuse visual and inertial sensor to locate indoor environment. The authors describe the building of an embedded hardware platform to the best of their knowledge and compare the result with a mature method and POSAV310.

This paper proposes a VP evaluation function that is used to extract the most advantages in the intersection of a plurality of parallel lines. To improve the extraction speed of adjacent frame, the authors first proposed fusing the NGCP of the current frame and the calibrated pre-integration to estimate the NGCP of the next frame. The visual pose model was established using extinction VP and NGCP, calibration of inertial sensor. This theory offers the linear processing equation of gyroscope and accelerometer by the model of visual and inertial pose.