We describe an integrated navigation system based on Global Navigation Satellite Systems (GNSS), an Inertial Navigation System (INS) and terrestrial ranging technologies that can support accurate and seamless indoor-outdoor positioning. To overcome severe multipath disturbance in indoor environments, Locata technology is used in this navigation system. Such a “Locata-augmented” navigation system can operate in different positioning modes in both indoor and outdoor environments. In environments where GNSS is unavailable, e.g. indoors, the proposed system is designed to operate in the Locata/INS “loosely-integrated” mode. On the other hand, in outdoor environments, all GNSS, Locata and INS measurements are available, and all useful information can be fused via a decentralised Federated Kalman filter. To evaluate the proposed system for seamless indoor-outdoor positioning, an indoor-outdoor test was conducted at a metal-clad warehouse. The test results confirmed that the proposed navigation system can provide continuous and reliable position and attitude solutions, with the positioning accuracy being better than five centimetres.

This paper is the second part of a two-part research effort to find the optimal detector and estimator that minimise the integrity risk in Receiver Autonomous Integrity Monitoring (RAIM). Part 1 shows that for realistic navigation requirements, the solution separation RAIM method can approach the optimal detection region when using a least-squares estimator. This paper constitutes Part 2. It presents new methods to design Non-Least-Squares (NLS) estimators, which, in exchange for a slight increase in nominal positioning error, can substantially lower the integrity risk. A first method is formulated as a multi-dimensional minimisation problem, which directly minimises integrity risk, but can only be solved using a time-consuming iterative process. Parity space representations are then exploited to develop a computationally-efficient, near-optimal NLS-estimator-design method. Performance analyses for an example multi-constellation Advanced RAIM (ARAIM) application show that this new method enables significant integrity risk reduction in real-time implementations where computational resources are limited.

This paper presents a sensor fusion method for the Ultra-Tightly Coupled (UTC) Global Positioning System (GPS)/Inertial Navigation System (INS) integrated navigation. The UTC structure, also known as the deep integration, exhibits many advantages, e.g., disturbance and multipath rejection capability, improved tracking capability for dynamic scenarios and weak signals, and reduction of acquisition time. This architecture involves the integration of I (in-phase) and Q (quadrature) components from the correlator of a GPS receiver with the INS data. The Particle Filter (PF) exhibits superior performance as compared to an Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) in state estimation for the nonlinear, non-Gaussian system. To handle the problem of heavy-tailed probability distribution, one of the strategies is to incorporate the UKF into the PF as the proposal distribution, leading to the Unscented Particle Filter (UPF). The combination of an adaptive UPF and Fuzzy Logic Adaptive System (FLAS) is adopted for reducing the number of particles with sufficiently good results. The GPS tracking loops may lose lock due to the signals being weak, subjected to excessive dynamics or completely blocked. One of the principal advantages of the UTC structure is that a Doppler frequency derived from the INS is integrated with the tracking loops to improve the receiver tracking capability. The Doppler frequency shift is calculated and fed to the GPS tracking loops for elimination of the effect of stochastic errors caused by the Doppler frequency. In this paper, several nonlinear filtering approaches, including EKF, UKF, UPF and ‘FLAS assisted UPF’ (FUPF), are adopted for performance comparison for ultra-tight integration of GPS and INS. It is assumed that no outage occurs such that the inertial sensor errors can be properly corrected and accordingly the aiding information is working well. Two examples are provided for performance assessment for the various data fusion methods. The FUPF algorithm with Doppler velocity aiding demonstrates remarkable improvement, especially in the high dynamic environments, in navigation estimation accuracy with reduction of number of particles.

The Istanbul Strait is a challenging waterway for maritime traffic due to its rough topology, moderate to severe environmental conditions, and heavy local traffic. In particular, a total of 232 maritime accidents took place there between 2000 and 2011. In this study, generic fuzzy analytic hierarchy processes were used to assess the risk perception of stakeholders in the Istanbul Strait, including ship captains, maritime pilots and Vessel Traffic Services operators. These risk perceptions were then compared to the statistical maritime accident data, revealing a fundamental discrepancy between the risk perception and statistical data. Specifically, the area of the Straight with the highest number of accidents is perceived as relatively low-risk, whereas areas perceived as high-risk experience a lower number of accidents. Our results have implications for stakeholders as well as government agencies responsible for the safety of the Straight.

The Delay Locked Loop (DLL) tracking algorithm is one of the most widely used in GPS receivers. It uses different correlators such as the Early-Late Slope (ELS) correlator and High-Resolution Correlator (HRC) to mitigate code phase multipath. These techniques are effective for weak multipath environments but they may not be suitable for challenging multipath environments. The Multipath Estimating Delay Lock Loop (MEDLL) shows better performance than the classical methods. However, MEDLL still has limited capabilities in severe multipath environments. This paper introduces a robust multipath mitigation technique based on fast orthogonal search to obtain better delay estimation for GPS receivers. This research utilised a SPIRENT Global Navigation Satellite Systems (GNSS) simulator to compare the performance of the proposed method with other multipath mitigation techniques. Experimental results demonstrated that the performance of the proposed algorithm was better than the classical and advanced techniques under the multipath scenarios tested.

This paper presents the results of distance measurements performed with an AT86RF233 chip. It uses a combination of time of flight and phase shift measurements to perform ranging. The statistical parameters describing the ranging results are presented and an algorithm to process raw measurement data is proposed. The results show significant improvement in ranging accuracy.

A new Bayesian approach for multiple satellite faults detection and exclusion is proposed by introducing a classification variable to each satellite observation. If we treat this classification variable as random and assume a prior distribution for it, then a rule for satellite fault detection and exclusion based on the posterior probabilities of the classification variables is constructed under the framework of Bayesian hypothesis testing. Secondly, the Gibbs sampler is introduced to compute the posterior probabilities of the classification variables. Then the implementation for a Bayesian Receiver Autonomous Integrity Monitoring (RAIM) algorithm is designed with the Gibbs sampler. Finally, different schemes are designed to evaluate the performance of the new Bayesian RAIM algorithm in the case of multiple faults. We compare the method in this paper with the Range Consensus (RANCO) method. Experiments illustrate that the proposed algorithm in this paper is capable of detecting and eliminating multiple satellite faults, and the probability of correctly detecting faults is high.

Albatrosses are known for their extreme navigation performance enabling them to locate isolated breeding islands after long-distance migrations across open seas. Little is known about the migration of young albatrosses and how they reach the adults' navigation and foraging skills during the period of immaturity lasting several years and spent permanently flying across the open ocean. We tracked by satellite telemetry the dispersal and migration of 13 juvenile wandering albatrosses from the Crozet Islands during their first year at sea. The young albatrosses covered an average distance of 184,000 km during the first year, restricting their dispersal movement to the unproductive and low wind subtropical Indian Ocean and Tasman Sea. The juveniles initiated the migration by an innate phase of rapid dispersal encoded as a fixed flight direction assisted by southerly winds towards north and northeast. Thereafter each individual restricted its movement to a particular zone of the ocean that will possibly be used until they start breeding 7–10 years later and return in contact with breeding adults. This dispersal in young birds corresponds well with movements observed for adult non-breeding wandering albatrosses. The results show clearly an inherited ability to navigate back to already visited areas in young wandering albatrosses. The juvenile dispersal behaviour and migration at sea suggest a genetically based migration program, encoding navigation to a destination area used throughout the life.

In Strapdown Inertial Navigation System (SINS)/Odometer (OD) integrated navigation systems, OD scale factor errors change with roadways and vehicle loads. In addition, the random noises of gyros and accelerometers tend to vary with time. These factors may cause the Kalman filter to be degraded or even diverge. To address this problem and reduce the computation load, an Adaptive Two-stage Kalman Filter (ATKF) for SINS/OD integrated navigation systems is proposed. In the Two-stage Kalman Filter (TKF), only the innovation in the bias estimator is a white noise sequence with zero-mean while the innovation in the bias-free estimator is not zero-mean. Based on this fact, a novel algorithm for computing adaptive factors is presented. The proposed ATKF is evaluated in a SINS/OD integrated navigation system, and the simulation results show that it is effective in estimating the change of the OD scale factor error and robust to the varying process noises. A real experiment is carried out to further validate the performance of the proposed algorithm.

Demonstrations and local tests of several maritime autonomous surface ships (MASS) have recently been carried out. From a technological standpoint, MASS are becoming able to handle actual operations in certain sea areas. Since 2017, the MSC (Maritime Safety Committee) of the IMO (International Maritime Organization) has been discussing legal problems with the Regulations for Preventing Collisions at Sea (COLREGs) with regard to MASS operation. The purpose of this paper is to clarify the rules from the perspective of seafarers, who need to interpret COLREGs when dealing with MASS in ship handling situations on the sea, and also to discuss possible required amendments to COLREGs. This paper attempts to clarify the extent of current interpretations while also taking into account the answers to questionnaires received from 130 pilots, ship captains and navigation officers concerning COLREGs for MASS operation. Given the four common principles of COLREGs, it is considered whether the principles need to be changed with the introduction of MASS from the viewpoint of seafarers.

Automatic Dependent Surveillance Broadcast (ADS-B) is envisioned to support seamless aircraft surveillance and enhanced air-to-air and air-to-ground applications. ADS-B is an integrated system, dependent on on board navigation systems to obtain aircraft state information as well as a communication data link to broadcast this information to Air Traffic Control (ATC) on the ground and other ADS-B equipped aircraft. To quantify system safety, a good understanding of the potential failure modes of the system is vital. ADS-B system failure modes include those from the communication and navigation systems and human and environmental factors, as well as ADS-B-specific components. In this paper, potential failure modes of the ADS-B system are identified using an approach developed in this paper. The end output of the approach is an ADS-B failure mode register. However, the approach is transferable to other ATC surveillance systems. The paper further provides the failure classification and modelling, and also analyses the failure modes' impact on ATC operations and finally proposes potential mitigations. It is important to note that the work carried out in this paper is based on the assumption that the ADS-B operates as the primary surveillance source for the ATC.

Tracking dynamics on the GPS signal is still a big challenge to the receiver designer as the operating conditions are becoming more volatile. Optimizing the stand-alone system for dynamics generally degrades the accuracy of measurements. Therefore, an inertial navigation system (INS) is integrated with GPS to address this issue. Doppler derived from INS can be used to aid the carrier tracking loop for improving the performance under dynamic conditions. However, the derived doppler does not truly reflect the GPS signal doppler due to errors in inertial sensors. As the tracking loop bandwidth is reduced significantly in ultra-tightly integrated systems, any offsets in the aiding doppler creates undesired correlations in the tracking loop resulting in sub-optimal performance of the loop. The paper addresses this issue and also provides a mitigating mechanism to reduce the effects of incorrect estimates of the doppler. It is shown that doppler offsets resulting in a bias in the tracking loop can be appropriately modelled and removed. Mathematical algorithms pertaining to this are provided and the results are summarized. Simulations show that the bias due to aiding doppler offsets could be effectively addressed by appropriate modelling.

Navigational accidents (collisions and groundings) account for approximately 85% of mari-time accidents, and consequence estimation for such accidents is essential for both emergency resource allocation when such accidents occur and for risk management in the framework of a formal safety assessment. As the traditional Bayesian network requires expert judgement to develop the graphical structure, this paper proposes a mutual information-based Bayesian network method to reduce the requirement for expert judgements. The central premise of the proposed Bayesian network method involves calculating mutual information to obtain the quantitative element among multiple influencing factors. Seven-hundred and ninety-seven historical navigational accident records from 2006 to 2013 were used to validate the methodology. It is anticipated the model will provide a practical and reasonable method for consequence estimation of navigational accidents.

The container liner shipping industry has stepped into an era of international strategic alliances. Important to these liner alliances is the sharing and allocation of container slots between its member carriers. This paper optimises planning of container ship capacity sharing and co-allocation under a co-charting agreement. First, we explain the concept of this business agreement and its implications on maritime operations. Then, we identify key influencing factors that may affect the decisions of cooperative slot co-allocation. The slot co-allocation problem is modelled as an Integer Programming problem and solved using data from two routes between the United States and Asia. The model determines the optimal slot co-allocation strategies between shipping alliance carriers along allied shipping routes. Computational results indicate that the proposed method is effective in obtaining optimal, cooperative slot sharing strategies that can maximise the total system revenue.

Strapdown inertial navigation systems (INS) often employ aiding sensors to increase accuracy. Nonlinear filtering algorithms are then needed to fuse the collected data from these aiding sensors with measurements of strapdown rate gyros. Aiding sensors usually have slower dynamics compared to gyros and therefore collect data at lower rates. Thus the system will be unobservable between aiding sensors' sampling instants, and the error covariance, which shows the uncertainty in the estimation, grows during the sampling period. This paper presents a divided difference filter (DDF)-based data fusion algorithm, which utilizes the complementary noise profile of rate gyros and gravimetric inclinometers to extend their limits and achieve more accurate attitude estimates. It is confirmed experimentally that DDF achieves better covariance estimates compared to the extended Kalman filter (EKF) because the uncertainty in the state estimate is taken care of in the DDF polynomial approximation formulation.

Integer ambiguity resolution is able to improve positioning accuracy and reduce convergence time in Precise Point Positioning (PPP). Although significantly improved horizontal positioning accuracy has been demonstrated, the height solution improvement is found to be less significant, and improving this requires further investigation. In this paper, a troposphere constraint method using precise troposphere corrections is proposed to improve the PPP ambiguity-resolved height solution. This is different from the conventional approach that typically applies meteorological data to calculate the a priori troposphere delay and estimates the residual troposphere delay. The effects of the troposphere delay on PPP ambiguity-resolved height solutions are first studied. Numerical analysis is conducted to ambiguity-resolved positioning results based on the decoupled clock model and hourly Global Positioning System (GPS) observations from a Canadian PPP-inferred troposphere precipitable water vapour system. The results show that by using the proposed method the PPP ambiguity-resolved height accuracy can be further improved to 3·86 cm compared to 5·32 cm using the conventional approach.

The single initial Global Positioning System (GPS) has been expanded into multiple global and regional navigation satellite systems (multi-GNSS/RNSS) as the Global Navigation Satellite System (GLONASS) is restored and the BeiDou Navigation Satellite System (BDS), Galileo Satellite Navigation System (Galileo) and Quasi-Zenith Satellite System (QZSS) evolve. Using the differences among these five systems, the paper constructs a consolidated multi-GNSS/RNSS precise point positioning (PPP) observation model. A large number of datasets from Multi-GNSS Experiment (MGEX) stations are employed to evaluate the PPP performance of multi-GNSS/RNSS. The paper draws three main conclusions based on the experimental results. (1) The combined GPS/GLONASS/Galileo/BDS/QZSS presents the PPP with the shortest mean convergence time of 11·5 min, followed by that of GPS/GLONASS/Galileo/BDS (12·4 min). (2) The combined GPS/GLONASS/BDS/Galileo/QZSS shows the optimal PPP performance when the cut-off elevation angle is basically the same because of the rich observation data due to a large number of satellites. To be specific, for combined GPS/GLONASS/BDS/Galileo/QZSS, the PPP convergence percentage is 80·9% higher relative to other combined systems under 35° cut-off elevation angle, and the percentages of the root mean square values of PPP within 0–5 cm are enhanced by 80·5%, 81·5% and 87·3% in the North, East and Up directions relative to GPS alone at 35° cut-off elevation angle. (3) GPS alone fails to conduct continuous positioning due to the insufficiency of visible satellites at 40° cut-off elevation angle, while the kinematic PPP of multi-GNSS/RNSS remains capable of obtaining positioning solutions with relatively high accuracy, especially in the horizontal direction.

The notion of the earth as a perfect sphere has served navigators quite well over the centuries and it continues to provide a basis for instruction and practical navigation. Aside from the fact that modern navigators employ refined models of the earth such as the WGS84 ellipsoid, which together with satellite positioning gives unprecedented positioning accuracy, the question arises as to just how good are the spherical results for distance when compared to the results for distance obtained from the spheroidal model. In this document a comparison is made of great circle distance (GCD) on the sphere with great ellipse distance (GED) on the spheroid. This comparison is then repeated for rhumbline distances. In each case it is concluded that the difference in using the sphere when compared to the spheroid is near 0·5%.

Four methods to calculate the Vertical Protection Level (VPL) can be used in Advanced Receiver Autonomous Integrity Monitoring (A-RAIM), among which the ideal method is the strictest one. To obtain the ideal VPL satisfying the exact required integrity risk, the worst case bias with the maximum integrity risk is searched for. This investigation has found that the correct worst case highly depends on the choice of the input VPL. To gain the correct result, the computation becomes complex and the accuracy of the result is compromised. Therefore, a new procedure is designed with a new search: the maximum VPL is searched to encompass all possible bias sizes. Since VPL is calculated with a given integrity risk for each bias size, the uncertainty of the arbitrary VPL input in the ideal method is avoided. Also, an optimisation algorithm is adopted to improve computational efficiency. It is shown that the new method is more reliable and efficient than the current best method. Simulation results worldwide also show that the new approach has improved A-RAIM availability from 32%–38% to 74% with GPS and from 44%–43% to 85% with Galileo.