Search results

This study aims to address the problem of the divergence of traditional inertial navigation system (INS)/celestial navigation system (CNS)-integrated navigation for ballistic missiles. The authors introduce Doppler navigation system (DNS) and X-ray pulsar navigation (XNAV) to the traditional INS/CNS-integrated navigation system and then propose an INS/CNS/DNS/XNAV deep integrated navigation system.

DNS and XNAV can provide velocity and position information, respectively. In addition to providing velocity information directly, DNS suppresses the impact of the Doppler effect on pulsar time of arrival (TOA). A pulsar TOA with drift bias is observed during the short navigation process. To solve this problem, the pulsar TOA drift bias model is established. And the parameters of the navigation filter are optimised based on this model.

The experimental results show that the INS/CNS/DNS/XNAV deep integrated navigation can suppress the drift of the accelerometer to a certain extent to improve the precision of position and velocity determination. In addition, this integrated navigation method can reduce the required accuracy of inertial navigation, thereby reducing the cost of missile manufacturing and realising low-cost and high-precision navigation.

The velocity information provided by the DNS can suppress the pulsar TOA drift, thereby improving the positioning accuracy of the XNAV. This reflects the “deep” integration of these two navigation methods.

A full-order multi-objective anti-disturbance robust filter for SINS/GPS navigation systems with multiple disturbances is designed. Generally, the unmodeled dynamics, the external environmental disturbance and the inertial apparatus random drift may exist simultaneously in an integrated navigation system, which can be classified into three type of disturbances, that is, the Gaussian noise, the norm bounded noise and the time correlated noise. In most classical studies, the disturbances in integrated navigation systems are classified as Gaussian noises or norm bounded noises, where the Kalman filtering or robust filtering can be employed, respectively. While it is not true actually, such assumptions may lead to conservative results. The paper aims to discuss these issues.

The Gaussian noises, the norm bounded noises and the time correlated noises in the integrated navigation system are considered simultaneously in this contribution. As a result, the time correlated noises are augmented as a part of system state of the integrated navigation system error model, the relative integrated navigation problem can be transformed into a full-order multi-objective robust filter design problem for systems with Gaussian noises and norm bounded disturbances. Certainly, the errors of the time correlated noises are estimated and compensated for high precision navigation purpose. Sufficient conditions for the existence of the proposed filter are presented in terms of linear matrix inequalities (LMIs) such that the system stability is guaranteed and the disturbance attenuation performance is achieved.

Simulations for SINS/GPS integrated navigation system given show that the proposed full-order multi-objective anti-disturbance filter, has stronger robustness and better precision when multiple disturbances exist, that is, the present algorithm not only can suppression the effect of white noises and norm bounded disturbance but also can estimate and compensate the modeled disturbance.

The proposed algorithm has stronger anti-disturbance ability for integrated navigation with multiple disturbances. In fact, there exist multiple disturbances in integrated navigation system, so the proposed scheme has important significance in applications.

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.

In this study, the integrated navigation system, consisting of radio and inertial navigation system (INS) altimeters, is presented. INS and the radio altimeter have different benefits and drawbacks. The integration is achieved by using an indirect Kalman filter. Hereby, the error models of the navigators are used by the Kalman filter to estimate vertical channel parameters of the navigation system. In the open loop system, INS is the main source of information, and radio altimeter provides discrete aiding data to support the estimations. At the next step of the study, in case of abrupt faults, the performance of the integrated system is examined. The optimal Kalman filter reacts with abnormal estimates to this situation as expected. To recover such a possible malfunctioning, the robust Kalman filter algorithm is suggested.

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.

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 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 Embedded GPS/INS System (EGI) has been used more widely as central navigation equipment of aircraft. For certain cases needing high attitude accuracy, star sensor can be integrated with EGI to improve attitude performance. Since the filtering‐correction loop has already built in finished EGI product, centralized or federated Kalman filter is not applicable for integrating EGI with star sensor; it is a challenge to design multi‐sensor information fusion algorithm suitable for this situation. The purpose of this paper is to present a double‐layer fusion scheme and algorithms to meet the practical need of constructing integrated multi‐sensor navigation system by star sensor assisting finished EGI unit.

The alternate fusion algorithms for asynchronous measurements and the sequential fusion algorithms for synchronous measurements are presented. By combining alternate filtering and sequential filtering algorithms, a kind of double‐layer fusion algorithms for multi‐sensors is proposed and validated by semi‐physical test in this paper.

The double‐layer fusion algorithms represent a filtering strategy for multiple non‐identical parallel sensors to assist INS, while the independent estimation‐correction loop in EGI is still maintained. It has significant benefits in updating original navigation system by integrating new sensors.

The approach described in this paper can be used in designing similar multi‐sensor information fusion navigation system composed by EGI and various kinds of sensors, so as to improve the navigation performance.

Compared with conventional approach, in the situation that centralized and federated Kalman filter are not applicable, the double‐layer fusion scheme and algorithms give an external filtering strategy for measurements of finished EGI unit and star sensors.

The purpose of this paper is to improve the tracking performance of the carrier phase lock loop (PLL) in the strapdown inertial navigation system/global positioning system (SINS/GPS) integrated system with an innovative scheme of ultra‐tight integration.

First, providing the Doppler frequency for PLL using SINS velocity could enlarge the loop equivalent bandwidth and reduce the dynamic effect on the carrier loop. Meanwhile, lowering the filter bandwidth could increase the immunity to noise. Second, the relationships between the PLL and SINS errors have been analyzed, and then the PLL error model is established to eliminate the correlation between the pseudo‐range‐rate error and SINS velocity error. Third, the carrier frequency is regulated to improve the tracking accuracy, according to the error estimations of Kalman filter.

The innovative ultra‐tightly integrated system could not only enhance the anti‐jamming capability and the dynamic tracking performance of the tracking loops, but also improve the pseudo‐range‐rate measurements accuracy for the integrated filter.

This paper provides further study on the method of enhancing the carrier‐tracking performance and improving the integration mode in the ultra‐tightly integrated system based on the software‐defined GPS receiver.

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.