To achieve autonomous all-day flight by high-altitude long-endurance unmanned aerial vehicle (HALE UAV), a new navigation method with deep integration of strapdown inertial measurement unit (SIMU) and triple star sensors based on atmospheric refraction correction is proposed. By analysing the atmospheric refraction model, the stellar azimuth coordinate system is introduced and the coupling relationship between attitude and position is established. Based on the geometric relationship whereby all the stellar azimuth planes intersect on the common zenith direction, the sole celestial navigation system (CNS) method by stellar refraction with triple narrow fields of view (FOVs) is studied and a loss function is built to evaluate the navigation accuracy. Finally, the new SIMU/triple star sensors deep integrated navigation method with refraction correction upgraded from the traditional inertial navigation system (INS)/CNS integrated method can be established. The results of simulations show that the proposed method can effectively restrain navigation error of a HALE UAV in 24 h steady-state cruising in the stratosphere.

In the integrated navigation system using extended Kalman filter (EKF), the state error conventionally uses linear approximation to tackle the commonly nonlinear problem. However, this error definition can diverge the filter in some adverse situations due to significant distortion of the linear approximation. By contrast, the nonlinear state error defined in the Lie group satisfies the autonomous equation, which thus has distinctively better convergence property. This work proposes a novel strapdown inertial navigation system (SINS) nonlinear state error defined in the Lie group and derives the SINS equations of the Lie group EKF (LG-EKF) for the MIMU/GNSS/magnetometer integrated navigation system. The corresponding measurement equations are also derived. A land vehicle field test has been conducted to evaluate the performance of EKF, ST-EKF (state transformation extended Kalman filter) and LG-EKF, which verifies LG-EKF's superior estimation accuracy of the heading angle as well as the other two horizontal angles (pitch and roll). The LG-EKF proposed in this paper is unlimited in the choice of sensors, which means it can be applied with both high-end and low-end inertial sensors.

The performance of Global Navigation Satellite System (GNSS) and Micro-Electro-Mechanical System (MEMS)-based Inertial Navigation System (INS) integrated navigation is reduced during GNSS outages. To bridge the period during GNSS outages, a novel hybrid intelligent algorithm incorporating a Discrete Grey Predictor (DGP) and a Multilayer Perceptron (MLP) neural network (DGP-MLP) is proposed. The DGP-MLP is used to provide a pseudo-GNSS position to correct the INS errors during GNSS outages; the DGP uses the GNSS position information of the latest few moments to predict the position of future moments; in the process of DGP-MLP, the MLP is used to modify the prediction errors of DGP, and the MLP is improved by adding momentum terms and adaptively adjusting the learning rate and momentum factor. To evaluate the effectiveness of the proposed methodology, four GNSS outages in different cases over a real field test data were employed. The experimental results demonstrate that the proposed methodology can significantly improve positioning accuracy during GNSS outages.

Precise Point Positioning/Inertial Navigation System (PPP/INS) integrated navigation based on PPP and low-accuracy INS is often used to provide position and attitude information for vehicle-mounted or airborne mobile mapping systems. With proper processing, the position accuracy of PPP/INS is comparable to that of Differential Global Navigation Satellite System (DGNSS)/INS, but the accuracy of the attitude, especially the yaw angle, cannot be guaranteed. However, the yaw angle is crucial for mobile mapping systems. To compensate for the insufficiency of PPP/INS, we have designed a Multi-Antenna GNSS (MAGNSS)/INS integrated navigation system. First, the attitude determination method using MAGNSS is presented in detail. Then, the MAGNSS attitude is combined with the PPP position and velocity as measurements for integration with the INS. Thus, PPP/INS integrated navigation was improved to MAGNSS/INS integrated navigation. Finally, a three-hour car-borne test was conducted to evaluate the performance of the proposed method. The results indicate that the attitude determined from MAGNSS is accurate and stable over time. Compared to PPP/INS, MAGNSS/INS integrated navigation can improve the attitude accuracy significantly because of the inclusion of MAGNSS attitude.

The accuracy and fault tolerance of filters are directly affected by the filter architecture and algorithm, thus influencing navigation performance. The chi square detection used in the conventional reset federated filter is not sensitive to soft faults, and it is easy to cause the health subsystem to be polluted through information sharing. It is a challenge to design an adaptive reset federated filter to improve the performance of the navigation system. Therefore, taking the Strapdown Inertial Navigation System/Global Positioning System/Celestial Navigation System/Synthetic Aperture Radar (SINS/GPS/CNS/SAR) integrated navigation system as an example, an adaptive federated filter architecture for vector-formed information sharing without a fault isolation module is designed in this paper. The proposed method uses the two-state chi square detection algorithm to calculate the parameters corresponding to each state, making the state with higher accuracy obtain a greater information distribution coefficient. In addition, according to the value of vector-formed information sharing, an adaptive coefficient of measurement noise is designed. This improves the adaptability of the navigation system to soft faults. Simulation results show that the accuracy of the proposed algorithm has the same performance compared with the conventional method under normal circumstances. When the sensor has a soft fault, the adaptive federated filter algorithm proposed in this paper can adaptively adjust the distribution coefficients, eliminate the influence of the fault information and improve the precision of the navigation system. The approach described in this paper can be used in multi-sensor integrated navigation. It will have better performance in engineering applications.

In order to utilise the position and attitude information of a Celestial Navigation System (CNS) to aid a Strapdown Inertial Navigation System (SINS) and make it possible to achieve long-range and high-precision navigation, a new SINS/CNS integrated navigation scheme based on overall optimal correction is proposed. Firstly, the optimal installation angle of the star sensor is acquired according to the geometric relationship between the refraction stars area and the star sensor's visual field. Secondly, an analytical method to determine position and horizontal reference is introduced. Thirdly, the mathematical model of the SINS/CNS integrated navigation system is established. Finally, some simulations are carried out to compare the navigation performance of the proposed SINS/CNS integrated scheme with that of the traditional gyro-drift-corrected integration scheme. Simulation results indicate that in the proposed scheme, without the aid of SINS, CNS can provide attitude and position information and the errors of the SINS are able to be estimated and corrected efficiently. Therefore, the navigation performance of the proposed SINS/CNS scheme is superior to that of a more traditional scheme in long-range flight.

In this contribution, an improved Extended Kalman Filter (EKF), named the Total Extended Kalman Filter (TEKF) is proposed for integrated navigation. It can consider the neglected random observed quantities which may appear in a dynamic model. In particular, this paper will consider the case of vision-based navigation. This algorithm is equipped with quadratic constraints and makes use of condition equations. The paper will show that the refined data from different sensors including a Global Positioning System (GPS) receiver, an Inertial Navigation System (INS) and remote sensors can be fused into a Constrained Total Extended Kalman Filter (CTEKF) algorithm. The CTEKF algorithm is applied to a case study in the Guilan province in the north of Iran. The results show the efficiency of the proposed algorithm.

For underwater vehicle navigation sensors, the output signal periods are different and time-varying. This would result in the decline of precision, and even wrong results. To deal with the problem, this paper puts forward a multi-sensor, adjustable-period integrated navigation method based on multi-stage signal trigger. This method considers the valid signals of a Doppler Velocity Log (DVL) as the trigger signals of a Dead-Reckoning (DR) navigation program. It also considers the valid signals of an acoustic positioning sensor as the trigger signals of the integrated navigation program. In this method, it can adjust the filtering period in real time. According to the time label of signals, this method actualises the time-space alignment of sensors. Then it conducts DR navigation and integrated navigation. The method can not only utilise the valid signals of each sensor sufficiently but also fuses the data based on time-space alignment efficiently. Sea trial data shows that when the output signal periods are certain, navigation precision of the method in this paper is better than a non-adjustable-period filtering method. Moreover, in poor conditions, it can also attain a high precision.

An X-ray pulsar/starlight Doppler deeply-integrated navigation method is proposed in this paper. A starlight Doppler measurement-aided phase propagation model, which can remove the orbital effect within the recorded photon Time Of Arrivals (TOAs), is derived, and guarantees that the pulse phase can be extracted from the converted photon TOAs using computationally efficient methods. Some simulations are performed by a hardware-in-loop system to verify the performance of the integrated pulse phase estimation method as well as of the integrated navigation method. The integrated pulse phase estimation method could achieve an estimation performance similar to the existing method for orbiting vehicles at the cost of much less computational complexity, is capable of handling the signals of millisecond pulsars, and is applicable to various vehicles. In addition, the proposed integrated navigation method could provide reliable positioning results for various vehicles.

Gradual fault detection is always an important issue in integrated navigation systems, and the gradual fault is the most difficult fault to detect. To detect gradual faults in a timely and precise manner in integrated navigation systems, the statistical concepts of the normalised residual mean and the sum of absolute residuals are introduced according to the characteristics of gradual system failure in this paper. The applicability of the improved residual χ2 detection method is discussed. Then, the gradual fault detection program based on the improved residual χ2 detection method is designed with the criterion of normalised residual mean and the sum of absolute residual. The simulation results and vehicle tests show that: 1) The residual of the failed sub-system can be calculated accurately with the improved residual χ2 detection method, which has strong applicability in gradual fault detection; 2) The gradual fault can be detected in a short time by using the normalised residual mean and the sum of absolute residual.

This paper presents the results of research on the fusion of tracking radar and an Automatic Identification System (AIS) in an Electronic Chart Display and Information System (ECDIS). First, the concept of these systems according to the International Maritime Organization (IMO) is described, then a set of theoretical information on radar tracking and the fusion method itself is given and finally numerical results with real data are presented. Two methods of fusion, together with their parameters, are examined. A proposal for calculating the covariance matrix for radar and AIS data is also given, and the paper ends with conclusions.

This paper considers the estimation of the process state and noise parameters when the statistics of the process and measurement noise are unknown or time varying in the integration system. An adaptive Kalman Filter (AKF) with a recursive noise estimator that is based on maximum a posteriori estimation and one-step smoothing filtering is proposed, and the AKF can provide accurate noise statistical parameters for the Kalman filter in real-time. An exponentially weighted fading memory method is introduced to increase the weights of the recent innovations when the noise statistics are time varying. Also, the innovation covariances within a moving window are averaged to correct the noise statistics estimator. Experiments on the integrated Strapdown Inertial Navigation System (SINS)/ Doppler Velocity Log (DVL) system show that the proposed AKF improves the estimation accuracy effectively and the AKF is robust in the presence of vigorous-manoeuvres and rough sea conditions.

In this paper, we present a high-precision Mars entry integrated navigation algorithm under large uncertainties via a desensitised extended Kalman filter (DEKF). Firstly, a new six degree-of-freedom Mars entry dynamics model is derived based on the angular velocity outputs of a gyro, which is free of modelling errors in the aerodynamic and control torques. Secondly, both the accelerometer outputs and radio measurements between orbiters and entry vehicle are used as the observations embedded in a navigation filter to perform state estimation and suppress the measurement noise. Finally, a desensitised extended Kalman filter, exhibiting the desirable property of efficiently reducing the sensitivity of state variables with respect to model and parameter uncertainties, is adopted in order to overcome the adverse effects of initial state errors and uncertainties during Mars atmospheric entry and further improve entry navigation accuracy. The numerical simulation results show that the DEKF-based integrated navigation algorithm developed in this paper can achieve a better navigation performance with higher accuracy when compared with the standard extended Kalman filter (EKF)-based integrated navigation algorithm in the presence of larger state errors and parameter uncertainties.

Navigation and positioning system users are demanding greater accuracy and reliability in ever more challenging environments. This is driving a wave of rapid innovation, with the result that multisensor integrated navigation systems will become much more complex. This introduces a number of problems, including how to find the necessary expertise to integrate a diverse range of technologies, how to combine technologies from different organisations that wish to protect their intellectual property, and how to incorporate new navigation technologies and methods without having to redesign the whole system. It also makes it desirable to share development effort over a range of different applications. To address this, the feasibility of a modular approach to the design and development of multisensor integrated navigation and positioning systems is analysed. Assessments of the requirements of different user communities and the adaptability of the different navigation and positioning technologies to different contexts and requirements are presented. Based on this, the adoption of an open interface standard for modular integration is recommended and the issues to be resolved in developing that standard are outlined.

In order to enhance the independent viability of high-orbit satellites, an X-ray pulsar-based navigation (XNAV)/celestial navigation system (CNS) integrated navigation method is proposed. An improved kinematic and static filter is derived to fulfil data fusion that can obtain an optimal estimation for global use. In the filter, unscented transformation is used to reduce linearization error, and the technique of separate-bias is used to reduce the impacts of systematic errors in XNAV measurements. The results of simulations have shown that the proposed navigation system can reach a positioning accuracy of less than 100 m, an improved performance over separate XNAV and CNS.

A navigation integration processing scheme, called the strong tracking unscented Kalman filter (STUKF), is based on the combination of an unscented Kalman filter (UKF) and a strong tracking filter (STF). The UKF employs a set of sigma points by deterministic sampling, such that the linearization process is not necessary, and therefore the error caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. As a type of adaptive filter, the STF is essentially a nonlinear smoother algorithm that employs suboptimal multiple fading factors, in which the softening factors are involved. In order to resolve the shortcoming in traditional approach for selecting the softening factor through personal experience or computer simulation, a novel scheme called the fuzzy strong tracking unscented Kalman filter (FSTUKF) is presented where the Fuzzy Logic Adaptive System (FLAS) is incorporated for determining the softening factor. The proposed FSTUKF algorithm shows promising results in estimation accuracy when applied to the integrated navigation system design, as compared to the EKF, UKF and STUKF approaches.

The Kalman filter (KF) is a form of optimal estimator characterized by recursive evaluation, which has been widely applied to the navigation sensor fusion. Utilizing the KF requires that all the plant dynamics and noise processes are completely known, and the noise process is zero mean white noise. If the theoretical behaviour of the filter and its actual behaviour do not agree, divergence problems tend to occur. The adaptive algorithm has been one of the approaches to prevent divergence problems in the Kalman filter when precise knowledge on the system models is not available. Two popular types of adaptive Kalman filter are the innovation-based adaptive estimation (IAE) approach and the adaptive fading Kalman filter (AFKF) approach. In this paper, an approach involving the concept of the two methods is presented. The proposed method is a synergy of the IAE and AFKF approaches. The ratio of the actual innovation covariance based on the sampled sequence to the theoretical innovation covariance will be employed for dynamically tuning two filter parameters – fading factors and measurement noise scaling factors. The method has the merits of good computational efficiency and numerical stability. The matrices in the KF loop are able to remain positive definitive. Navigation sensor fusion using the proposed scheme will be demonstrated. Performance of the proposed scheme on the loosely coupled GPS/INS navigation applications will be discussed.

There are many scenarios where an integrated INS/GNSS navigation system may be required to operate in a high interference or weak signal environment. The GPS receiver may exploit the inertial aiding by operating with narrow tracking loop bandwidths in order to increase interference resistance. However, where a low grade INS is used, wider bandwidths are desirable to calibrate the INS errors effectively. This is important for GPS tracking loop aiding and sole-means inertial navigation during jamming. To obtain both effective INS calibration and jamming resistance, an adaptive tightly-coupled (ATC) INS/GPS integration architecture has been developed. The ATC technique has been assessed by simulation, showing that it provides a significant anti-jam margin over an INS/GPS with fixed tracking bandwidths selected for INS calibration. Compared to the deep (or ultra-tightly-coupled) integration techniques currently under development, ATC is a low cost anti-jam integration technique as it does not require a complete re-design of the navigation architecture. When there is too much interference for any GNSS signals to be tracked, the INS provides sole-means navigation. Thus, it is important to optimise the calibration of the INS when GNSS signals are available. To this end, the effects of estimating higher order inertial instrument errors and satellite range biases within the INS/GPS integration filter have been assessed.

An adaptive integrated Kalman filtering based on the adjustment outputs of local navigation sensors and the outputs of a dynamic or kinematic state model is presented, which avoids the correlations of the local Kalman filtering outputs affected by the same disturbances of the dynamic state model. It has the advantage of rigor in theory and simple in calculation as well as adaptive in the various local navigation outputs. An integrated navigation estimator that is similar to the federated Kalman filtering is given as an initial estimate of a state based on the information sharing principle, but without any dynamic model information. An adaptive integrated fusion of the local navigation outputs and the dynamic model information is followed, in which the weights of the local navigation outputs and the dynamic model outputs are determined based on their differences from the integrated navigation results. The processing algorithms, logic, and associated computer burden are similar to those of federated filter. A simulated example is given to show the effectiveness of the new adaptive integrated navigation algorithm.