Fluctuation of the received signal strength (RSS) is the key performance-limiting factor for Wi-Fi indoor positioning schemes. In this study, the Manhattan distance was used in the weighted K-nearest neighbour (WKNN) algorithm to improve positioning accuracy. Reference point (RP) intervals were optimised to reduce the complexity of the system. Specifically, two new positioning schemes are proposed in this paper. Scheme 1 uses the cellular network to refine the fingerprint database, while Scheme 2 uses the cellular network positioning to locate the node a priori, then uses the Wi-Fi network to further improve accuracy. The experimental results showed that the average positioning error of Scheme 1 was 1·60 m, a reduction of 12% compared with the existing Wi-Fi fingerprinting schemes. In Scheme 2, when double cellular networks were used, RP usage was reduced by 64% and the calculating time was 0·24 s, a reduction of up to 69·5% compared with the Manhattan-WKNN algorithm. These proposed schemes are suitable for high accuracy and real-time positioning situations, respectively.

The remarkably successful development in recent years of inertial navigation systems suggests a reconsideration in this light of the phenomenon of animal navigation, the physiological basis of which has not been fully established. Some of the aspects of inertial navigation which may be particularly relevant to possible biological analogues are considered. Aspects of animal navigation and orientation that are particularly suggestive of inertial navigation are also treated. The physiology of the biological sensory mechanisms that could form the sensing elements of the biological inertial systems are outlined. Some suggestions are advanced for further investigations.

In this paper, a novel Strong Tracking Sigma-Point Predictive Variable Structure Filter (ST-SP-PVSF) is presented as a further development of the Adaptive Predictive Variable Structure Filter (APVSF) for attitude synchronisation during Satellite Formation Flying (SFF). First, the sequence orthogonal principle is adopted to enhance the robustness of the APVSF for any nonlinear system with uncertain model errors. Then, sigma-point sampling strategies (such as unscented transfer, cubature rule and Stirling's polynomial interpolation) are introduced to extend the APVSF with the ability to capture the second central moment's information on the model errors to update the system model with higher precision. The new methodology has advantages in dealing with the various types of uncertainties or model errors compared with the APVSF. In addition, it does not need to choose the limit boundary layer ψlim it for system estimation, which reduces the sensitivity to the initial parameters and improves its adaptive ability over the APVSF. Simulations are performed to demonstrate that the proposed method is more suitable for attitude synchronisation estimation of the SFF system.

This paper proposes an algorithm for the initial alignment method for rocket navigation systems. It uses the inertial freezing alignment to resolve the attitude matrix with respect to its fast and robust characteristics. Due to disturbances from the swaying base environment, such as people walking and wind effect, which would consequently result in a great lever arm effect, a Finite Impulse Response (FIR) filter is utilized to decrease the noise in the accelerometers' measurement. In addition, there are sensor errors in the system; the online estimation of gyroscopes' drift with a Kalman filter is adopted to achieve compensation. Numerical results from a simulated rocket initial alignment experiment are reported to demonstrate the effectiveness of the method.

This paper was first published in Volume 19, p. 88, in 1966. Parts II and III were included in following issues of the Journal. The paper is Crown Copyright and is reproduced with the permission of H.M. Stationery Office. It is followed by comments from Stanley Ratcliffe.

The main task of air traffic controllers is to plan traffic flows so that aircraft are allotted sufficient separation to absorb not only systematic differences in speed but also the imperfections of navigation and piloting, which we term flying errors. To this end, they usually work with three separation standards, to be applied in, respectively, the along-track, across-track and vertical dimensions of space. The separation standard for any one dimension is the minimum intended (i.e. planned) separation permitted in that dimension when the intended separations are less than standard in the other two. The problem is to choose standards which are safe enough, but not so large as to lead to unnecessary traffic delays and deviations.

The advent of multiple Global Navigation Satellite System (GNSS) constellations will result in a considerable increase in the number of satellites for positioning worldwide. This substantial improvement in measurement redundancy has the potential to radically advance receiver autonomous integrity monitoring (RAIM) performance. However, regardless of the number of satellites, the performance of existing RAIM methods is sensitive to the assumed prior probabilities of individual fault hypotheses. In this paper, a new method is developed using Bayes’ theorem to generate upper bounds on posterior probabilities of individual fault hypotheses given current user measurements. These bounds are used in a Bayesian fault-tolerant position estimator (FTE) that minimizes integrity risk. The detection test statistic is a measurement-based integrity risk bound, which is directly compared with a pre-specified risk requirement. The associated challenge of quantifying continuity risk is resolved using a bounding approach, which is also detailed in this work. The new Bayesian FTE method is shown to be more robust to uncertainty in prior probability of fault occurrence than existing RAIM methods.

Perhaps the most surprising thing about sea waves is that they come in a vast range of shapes and sizes. The casual observer on a ship in waters not exposed to an ocean, for example the southern North Sea, may rightly think that the waves he can see have all been generated by the same wind blowing over some particular stretch of water for a fixed length of time. It then seems almost logical to deduce that all the waves ought to be of the same height, length and shape. Unfortunately this is not the case, the energy of sea waves is locked in wave components spread over a wide range of wave periods, each of which travels at a speed dictated by its period. Considering the very simple case of a sea with only wave components, when a crest of one component overtakes the other, a higher wave will ensue. As a result of this process, high waves come in groups; during the time in which the components gradually get into phase the wave height builds up giving a train of waves of increasing, which then decreases as the faster component travels away, until when they are out of phase the sea is temporarily fairly calm. This is the reason why it is commonly said that every seventh wave is the highest, although whether it is every fourth or every fourteenth depends on the relative speeds of the components.

Superimposing Electronic Navigational Chart (ENC) data on marine radar images can enrich information for navigation. However, direct image superposition is affected by the performance of various instruments such as Global Navigation Satellite Systems (GNSS) and compasses and may undermine the effectiveness of the resulting information. We propose a data fusion algorithm based on deep learning to extract robust features from radar images. By deep learning in this context we mean employing a class of machine learning algorithms, including artificial neural networks, that use multiple layers to progressively extract higher level features from raw input. We first exploit the ability of deep learning to perform target detection for the identification of marine radar targets. Then, image processing is performed on the identified targets to determine reference points for consistent data fusion of ENC and marine radar information. Finally, a more intelligent fusion algorithm is built to merge the marine radar and electronic chart data according to the determined reference points. The proposed fusion is verified through simulations using ENC data and marine radar images from real ships in narrow waters over a continuous period. The results suggest a suitable performance for edge matching of the shoreline and real-time applicability. The fused image can provide comprehensive information to support navigation, thus enhancing important aspects such as safety.

It is well known that GPS measurements are regularly obstructed in urban environments. Positioning accuracy in such environments is significantly degraded and in many areas, it is not possible to obtain a GPS position fix at all. There are currently two methods that can be used to improve availability in the urban environment. Firstly, GPS receivers can be augmented with dead reckoning sensors such as an INS. Alternatively, High Sensitivity GPS (HSGPS) receivers can be used which are able to acquire and track very weak signals. This paper assesses the performance obtained from a GPS and low cost INS integrated system and a HSGPS receiver in an urban environment in Nottingham, UK. The navigation systems are compared to a high accuracy integrated GPS/INS system which is used to provide a reference trajectory. It is demonstrated that the differential GPS and low cost INS system can provide horizontal positioning accuracy of better than 2·5 m RMS in real-time, and better than 1 m RMS in post-processing, whereas the non-differential HSGPS receiver provides a real-time performance of 5 m RMS. These results were obtained in an environment where, with conventional GPS receivers, a position solution is only available 48·4% of the time. Operational considerations such as initial alignment of the GPS and low cost INS are also discussed when comparing the two systems for urban positioning applications.

Vehicular communication technologies are becoming staples of modern societies. This paper proposes a new positioning algorithm for vehicular networks. The algorithm is a non-classic Multi-Dimensional Scaling Filter (MDSF) that builds on a novel and computationally effective Multi-Dimensional Scaling (MDS) solution covariance estimation technique and also a Maximum Likelihood (ML) filter. In general a major drawback of the non-classic MDS is the high computational cost because of its iterative nature. It is shown that a special blend between vehicular Map-Matching (MM) and MDSF considerably reduces the number of iterations and the convergence time, making the MDSF a suitable algorithm for vehicular network positioning. The performance of MDSF is compared with that of an Extended Kalman Filter (EKF) together with the Cramar Rao Lower Bound (CRLB). It is shown through simulation that for all types of traffic conditions MDSF performs better than EKF and closer to CRLB than EKF. It is also shown that both MDSF and EKF algorithms are robust to typical Global Positioning System (GPS) outages in deep urban canyons. CRLB also proves that Cooperative Positioning (CP) in general has the ability to bridge short GPS outages.

Unmanned Marine Vehicles (UMVs), like their aerial cousins Unmanned Aerial Vehicles (UAVs), are not easily classified under existing legal regimes. Even though unmanned, should these seagoing drones be treated as ‘vessels’ under the Law of the Sea Convention articles on navigation rights and duties? Are they ‘vessels’ under the International Regulations for Preventing Collisions at Sea (COLREGs, 1972)? If so, should they be accorded a manoeuvring priority vis-à-vis other vessels? Are the differences between autonomous UMVs and the increasingly automated manned vessels all that great, such that classification should turn on whether the vessel is manned rather than on how navigation and collision avoidance decisions are made and executed?

Facilitated by the establishment of terrestrial networks and satellite constellations of Automatic Identification System (AIS) receivers, large quantities of spatial and temporal information that trace ships' paths have been collected. The exponential increase in the amount of AIS data has caused expensive and time-consuming transmission, calculation and storage problems. Using appropriate trajectory simplification methods in a timely manner to compress redundant information while minimising the loss of importation information is important. To minimise the simplification error, this paper proposes an online multi-dimensional simplification algorithm for AIS trajectory streaming data. The simplification algorithm takes into account position, direction and speed preservation. Finally, a comparison experiment with other algorithms is made to examine the effectiveness of this algorithm. The results indicate that the proposed online multi-dimensional simplification algorithm can effectively preserve a ship's motion state, including its position, speed and course.

In this paper, a triangle matching algorithm using local gravity field maps is proposed to bound the drift errors inherent in Strapdown Inertial Navigation Systems (SINS) in gravity-aided navigation. This triangle matching algorithm has two main stages, the first is the initial matching stage, which has a coarse phase and a fine phase to address the large unknown initial errors made by INS, and the other is the tracking matching stage, which mainly aims at tracking the matching solution with the vehicle running in real time. Simulations were carried out using data for the Bohai Sea and South China Sea areas, to assess the effects of different initial errors on the matching solutions. Finally some experiments were carried out to evaluate the proposed algorithm. The results show that the triangle matching algorithm has some compelling advantages, such as a capability to address the large unknown initial errors made by INS, and good real-time quality of matching the gravity measurements with the local gravity maps.

In this study, we develop a Multi-constellation Global Navigation Satellite System (GNSS) Receiver Differential Code Bias (MGR-DCB) model. The model estimates the receiver DCBs for the Global Positioning System (GPS), BeiDou and Galileo signals from the ionosphere-corrected geometry-free linear combinations of the code observations. In order to account for the ionospheric delay, a Regional Ionospheric Model (RIM) over Europe is developed. GPS observations from 60 International GNSS Servoce (IGS) and EUREF reference stations are processed in the Bernese-5·2 Precise Point Positioning (PPP) module to estimate the Vertical Total Electron Content (VTEC). The RIM has spatial and temporal resolutions of 1° × 1° and 15 minutes, respectively. The receiver DCBs for three stations from the International GNSS Service Multi-GNSS Experiment (IGS-MGEX) are estimated for three different days. The estimated DCBs are compared with the MGEX published values. The results show agreement with the MGEX values with mean difference and Root Mean Square Error (RMSE) values less than 1 ns. In addition, the combined GPS, BeiDou and Galileo VTEC values are evaluated and compared with the IGS Global Ionospheric Maps (IGS-GIM) counterparts. The results show agreement with the GIM values with mean difference and RMSE values less than 1 Total Electron Content Unit (TECU).

In underwater navigation, the conventional Error State Kalman Filter (ESKF) is used for combining navigation data where due to first order linearization of the nonlinear equations of the dynamics and measurements, considerable error is induced in estimated error state and covariance matrices. This paper presents an underwater integrated inertial navigation system using the unscented filter as an improved nonlinear version of the Kalman filter family. The designed system consists of a strap-down inertial navigation system accompanying Doppler velocity log and depth meter. In the proposed approach, to use the nonlinear capabilities of the unscented filtering approach the integrated navigation system is implemented in a direct approach where the nonlinear total state dynamic and and measurement models are utilised without any linearization. To our knowledge, no results have been reported in the literature on the experimental evaluation of the unscented-based integrated navigation system for underwater vehicles. The performance of the designed system is studied using real measurements. The results of the lake test show that the proposed system estimates the vehicle's position more accurately compared with the conventional ESKF structure.

Bathymetric Electronic Navigational Charts (bENCs) contain only bathymetry data and can be used in applications such as underwater positioning, dredging and piloting. According to International Hydrographic Organization (IHO) standard S-57, Electronic Navigational Charts (ENCs) contain depth information with pure density of depth contours. Typical depth contours encoded by Hydrographic Offices are limited to 2, 5, 10 and 20 m. Availability of more depth contours in bENCs would allow the visualisation of a safety contour which is closer to users' specific needs, especially in restricted waters such as ports, lakes and rivers. Another problem is non – Global Navigation Satellite System (GNSS) Unmanned Underwater Vehicle (UUV) navigation. bENCs could be used as reference data for UUV comparative navigation. This is called terrain reference navigation. This article presents the results from bathymetric data processing that was performed to convert data contained in bENCs into a reference for underwater comparative navigation. We use data obtained using a multibeam echo sounder to produce depth data with a horizontal spacing of 0·10 m that is suitable for use in restricted waters. The experimental data was collected in and around the Port of Gdansk, Poland.

In some Global Positioning System (GPS) signal propagation environments, especially in the ionosphere and urban areas with heavy multipath, GPS signal encounters not only additive noise but also multiplicative noise. In this paper we compare and contrast the conventional GPS signal acquisition method which focuses on handling GPS signal acquisition with additive noise, with the enhanced GPS signal processing under multiplicative noise by proposing an extension of the GPS detection mechanism, to include the GPS detection model that explains detection of the GPS signal under additive and multiplicative noise. For this purpose, a novel GPS signal detection scheme based on high order cyclostationarity is proposed. The principle is introduced, the GPS signal detection structure is described, the ambiguity of initial PseudoRandom Noise (PRN) code phase and Doppler shift of GPS signal is analysed. From the simulation results, the received GPS signal at low power level, which is degraded by additive and multiplicative noise, can be detected under the condition that the received block of GPS data length is at least 1·6 ms and sampling frequency is at least 5 MHz.

Loran-C receivers measure time delay differences in the signals they receive, compute differences of distance, and hence determine the user's position. The conversion from time to distance requires knowledge of the signals' velocities, which differ when propagating over sea or land. Precise positioning requires the delays of land paths to be accurately mapped, a procedure traditionally entailing expensive and time-consuming marine surveys. The resulting data, in the form of Additional Secondary Factors (ASFs), may be stored in Loran receivers. This paper introduces the concept of Loran-C Additional Secondary Factors, and shows how it is possible to map them efficiently. It focuses on recent advances, chiefly the development of powerful software for modelling the growth of ASFs over mountainous terrain. This employs an implementation of an algorithm proposed by Monteath. The modelling capability of the algorithm is demonstrated by examining propagation along a route that crosses mountains and deep fjords. In addition, the ASFs it predicts are compared with sample measurements made around the west coast of Scotland.

In this paper, some improvements in feature extraction for geomagnetic matching are made. Geomagnetic entropy is extracted from the total intensity of the geomagnetic field and its performance for navigation is tested with real geomagnetic data. The matching progress is divided into two phrases: rough matching and precise matching. In the rough matching phase, a sub-region that includes the real position is obtained by using the total intensity of the geomagnetic field as the matching feature. The entropy is applied to precise matching in the sub-region to obtain the final result. Simulations show that the navigation effect is significantly improved with the entropy as an assistant feature. The combined match probability is much better than the results derived from those using only total intensity as the matching feature. This is especially true when the flight vehicle passes through an area without abundant magnetic information.

In this study, an experiment to establish a model for human-environment social systems, a multi-agent simulation model to deal with urban traffic congestion problems involving automobiles embedded with several strategies of car navigation systems (CNS), is presented. A shortest time route with route information sharing strategy (ST-RIS) is believed to be one of the solutions for a novel CNS based on bilateral information shared among automobile agents. We assume several strategies including ST-RIS for agents, which are defined differently in terms of their information-handling process. The question of which strategy is most appropriate for solving urban traffic congestion can be seen as a social dilemma, because social holistic utility may conflict with an agent's individual utility. The presented model shows that this social dilemma can be observed as a typical chicken-type dilemma, or as a typical minority game, where an agent who has adopted a minority strategy can earn more utility compared to when other strategies are used. Consequently, the model has illustrated that shortest time route with partial route information sharing strategy (ST-pRIS), which is an advanced strategic form of ST-RIS in which only partial information is shared among agents, has moderate potential to be diffused in a society from the viewpoint of the evolutionary game theory.