GPS applications such as Precise Point Positioning (PPP) require the availability of precise ephemeris at high rate. To support these applications, several institutions such as the International GNSS Service (IGS) have developed precise orbital service. Unfortunately, however, the data rate of such precise orbits is usually limited to 15 minutes. To overcome this problem, a number of orbital interpolation methods are proposed. This paper examines the performance of four interpolation methods for IGS precise GPS orbits, namely Lagrange, Newton Divided Difference, Cubic Spline and Trigonometric interpolation. In addition, the paper discusses a new approach, which utilizes the residuals between the broadcast and precise ephemeris to generate a high density precise ephemeris. It is shown that the new approach produces better results than previously reported orbital interpolation accuracy.

Positioning in the urban environment using GNSS is hampered by poor satellite availability due to signal obstruction created by both man-made and natural features of the urban environment. In addition, range measurement to satellites for positioning and for navigation is severely degraded by the multipath effect. The arrival and continuous enhancement of computerised geometric city models makes it possible to tackle these problems through modelling. In this paper description is given of a method for determining the local multipath environment, defined by the surfaces within a city model that will cause disruptive signal reflections to be presented to a receiver. An example simulation is performed, and graphical and numerical results are produced.

The modelling of most Global Navigation Satellite System (GNSS) errors/biases and developments of data processing techniques have been improved substantially since the birth of Global Positioning System (GPS), however, there has been much less progress in the improvement of phase multipath mitigation techniques. Multipath therefore remains one of the most important error sources in high precision GNSS positioning. This is because multipath is site-dependent and therefore cannot be eliminated by differencing techniques. Also multipath is highly dependent on satellite-reflector-antenna geometry, which usually causes rapid changes in phase multipath errors especially in Real-Time Kinematic (RTK) applications. Multipath mitigation for static antennas such as those at reference stations can be carried out by site calibration, averaging over long observation times, and through the estimation of the error using filtering based on signal-to-noise ratio (SNR) data. However, multipath mitigation for kinematic antennas is still very difficult today.

Nevertheless, much research has been carried out on a particular class of phase multipath mitigation techniques: ones that can be applied within positioning algorithms (rather than incorporated into the receiver tracking loops or antennas). This paper investigates and further develops a number of state-of-the-art techniques in this category. They include phase multipath estimation using SNR data, phase multipath estimation through the use of closely spaced antennas, multipath mitigation stochastic models such as the satellite elevation angle model and SNR-based models (SIGMA-∊ model and our modified SNR-based model), and our own novel ray-tracing method. The techniques are tested with both real and simulated data, the real test datasets have been collected on the Laboratoire Central des Ponts et Chaussées (LCPC) testbed near Nantes in France, and on the campus of the University of Nottingham during SPACE data collection experiments.

Multipath is still one of the major error sources that degrades the accuracy of GPS positioning. The amount of multipath is highly dependent on the antenna's environment, which makes it difficult to isolate. Usually there is at least one in-view satellite which is more susceptible to multipath, particularly the one with the lowest elevation angle. To increase the positioning the best satellites must be selected (i.e. by least square or multipath mitigation) for computing a position. In this paper we propose an algorithm which picks up the best satellites (when there are more than four satellites in view) based on wavelet analysis for calculating a position. In this experiment, code and carrier measurements were collected in 15-minute segments by exploiting a single frequency (L1), stationary, navigation-grade receiver in a high-multipath environment. The magnitudes of these pseudoranges were often inflated by multipath error. We then post-processed the received data by applying wavelet filtering to the residuals (code minus carrier) to approximate the multipath values, and compute the receiver's position based on the selected satellites. Satellites were selected based on the residual values. To compare the results with the raw measurements, statistical elements were computed. The results showed significant improvement in variance of the estimated positions and, most importantly, a normalization of the data scatter-distribution was observed.

This paper proposes a numerical algorithm designed to integrate the GPS satellite perturbed orbit. The numerical solution is applied to integrate the differential equation of perturbed motion that frames the significant perturbing accelerations. Perturbing potentials are given and the corresponding accelerations in Cartesian coordinates are subsequently deduced. The C++ program that implements a fourth-order Runge-Kutta algorithm is described and comment is made on the perturbed orbit integration results. The paper offers a set of conclusions that will hopefully create a quantitative and qualitative image of GPS orbital perturbations and open a few ways ahead.

In the work package “Integrated Positioning” of the research project NAVIO (Pedestrian Navigation Systems in Combined Indoor/Outdoor Environments) we are dealing with the navigation and guidance of visitors to our University. The start points are the public transport stops in the surroundings of the Vienna University of Technology and the system users should be guided to certain office rooms or persons. For the user's position determination different location sensors are employed, i.e., for outdoor positioning GPS and dead reckoning sensors, such as a digital compass and gyro for heading determination, accelerometers for the determination of the distance travelled, a barometric pressure sensor for altitude determination and, for indoor areas, location determination using WiFi fingerprinting. All sensors and positioning methods are combined and integrated using a Kalman filter. An optimal estimate of the current location of the user is obtained using the filter. To perform an adequate weighting of the senors in the stochastic filter model, the sensor characteristics and performance were investigated in several tests. The tests were performed in different environments either with free satellite visibility, in urban canyons or inside buildings. The tests have shown that it is possible to determine the user's location continuously with the required precision and that the selected sensors provide a good performance and high reliability. Selected tests results and our approach are presented in the paper.

This paper focuses on modelling and predicting differential GPS corrections transmitted by marine radio-beacon systems using artificial neural networks. Various neural network structures with various training algorithms were examined, including Linear, Radial Biases, and Feedforward. Matlab Neural Network toolbox is used for this purpose. Data sets used in building the model are the transmitted pseudorange corrections and broadcast navigation message. Model design is passed through several stages, namely data collection, preprocessing, model building, and finally model validation. It is found that feedforward neural network with automated regularization is the most suitable for our data. In training the neural network, different approaches are used to take advantage of the pseudorange corrections history while taking into account the required time for prediction and storage limitations. Three data structures are considered in training the neural network, namely all round, compound, and average. Of the various data structures examined, it is found that the average data structure is the most suitable. It is shown that the developed model is capable of predicting the differential correction with an accuracy level comparable to that of beacon-transmitted real-time DGPS correction.

Indoor positioning imposes demanding requirements on the design of Global Navigation Satellite System (GNSS) sensors for both the acquisition and tracking functions. Although different combinations of coherent and non-coherent integration periods of a GNSS signal can be used to achieve reliable acquisition of the GNSS signals and indoors positioning, there are limitations to the extent that the integration period of the signal energy can be increased set by the receiver and satellite dynamics and the stability of the local oscillator. Assisting networks for GNSS applications (AGNSS) provide users with the capability of using long integration periods, enabling them to acquire indoor signals at low Carrier to Noise Ratio (CNR) values, where CNR is defined as the ratio of the received signal power over the noise density in units of dB-Hz. In this work we propose and evaluate the potential of a new method that will provide the user with an additional signal energy margin for accurate and reliable indoor positioning, with or without relying on assisted GNSS-type algorithms. The technique proposed here is based on the coherent and non-coherent combination of the energy of signals transmitted from the same GNSS satellite on different frequencies using the multiple open service signals that are to be provided by the Galileo system and under the GPS modernisation. This paper shows the improvement to the receiver acquisition and tracking performance using the proposed technique of combining energies at the L1, L2 and L5 bands for both data and pilot signals.

This article is partially based on the Doctoral thesis (Reference 1) entitled (in English) Working aboard and job-related accidents: Health and safety conditions on merchant vessel. Divided into four sections, the paper includes: issues relating to legislation, an analysis of accidents, the research that has been done about accidents and risk factors for falling on board. The main causes of accidents are examined. Moreover, conclusions are drawn by analysing work-related accidents in the Spanish maritime transport sector through a study covering the period between 1999 and 2003. The text was updated in 2004, with the latest official data, figures which may be equally applicable to the maritime sector throughout the European Union.

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.

The central task of GPS/INS integration is to effectively blend GPS and INS data together to generate an optimal solution. The present data fusion algorithms, which are mostly based on Kalman filtering (KF), have several limitations. One of those limitations is the stringent requirement on precise a priori knowledge of the system models and noise properties. Uncertainty in the covariance parameters of the process noise (Q) and the observation errors (R) may significantly degrade the filtering performance. The conventional way of determining Q and R relies on intensive analysis of empirical data. However, the noise levels may change in different applications. Over the past few decades adaptive KF algorithms have been intensively investigated with a view to reducing the influence of the Q and R definition errors. The covariance matching method has been shown to be one of the most promising techniques. This paper first investigates the utilization of an online stochastic modelling algorithm with regards to its parameter estimation stability, convergence, optimal window size, and the interaction between Q and R estimations. Then a new adaptive process noise scaling algorithm is proposed. Without artificial or empirical parameters being used, the proposed adaptive mechanism has demonstrated the capability of autonomously tuning the process noise covariance to the optimal magnitude, and hence improving the overall filtering performance.

This paper briefly outlines the production of electronic nautical charts (ENC) as a digital nautical chart, produced by the Croatian National Hydrographic Office (HO) to International Hydrographic Organization (IHO) specifications S-57 Edition 3.1. The production of ENCs is based on the theory of multiscale data management (usage bands). Multiple representations of ENC data are controlled by scale minimum (SCAMIN) attributes. A solution to the problem of multiscale data management as a part of ENC data production for archipelagic sea areas, using the East Adriatic Coast as an example is presented. This solution is based on a long-standing experience in the production of paper charts and recent ENC production for the eastern coast of the Adriatic Sea, which is believed to be the second largest archipelagic area in the Mediterranean. Finally, a new usage band scale range, compilation scale for all navigational purposes and method of using SCAMIN attributes for archipelagic seas are proposed.

One of the popular techniques for solving GPS pseudorange nonlinear quadratic equations to obtain the receiver's position and clock bias involves linearizing the equations and solving them by the least-squares (LS) scheme, which is based on the L2 minimization criterion. When outliers are a problem, LS estimation scheme may not be the best approach because the LS estimator minimizes the mean squared error of the observations. This paper discusses the implementation aspects of L1 and L∞ criteria, and their outlier resistance performance for GPS positioning. Three ordinary differential equation formulation schemes and corresponding circuits of neuron-like analogue L1 (least-absolute), L∞ (minimax), and L2 (least-squares) processors will be employed for GPS navigation processing. The circuits of simple neuron-like analogue processors are employed essentially for solving systems of linear equations. Experiments on single epoch and thereafter kinematic positioning will be conducted by computer simulation for investigating the outlier resistance performance for the least-absolute and minimax schemes as compared to the one provided by the least-squares scheme.

In this paper, we propose new separated decision variables that are derived by directly using the normal distribution of each measurement error and allowing substitution of the chi-square variable of the conventional method. In the derivation of the proposed decision variables, we considered not only the related mathematical model, but also the additional unmodelled properties of GPS measurements. Using the sequential pseudo-moving-average technique, we developed a method that easily obtains the combined results of multiple epochs. To verify our proposed algorithm, we analysed its performance using real data and compared the results with those of the conventional method. Our proposed approach performs better than the conventional approach, and effectively reduces computational effort by approximately 60%. Our results demonstrate that our method achieves a solution that is as reliable as the conventional technique, while reducing the time required to only 15% of that required by the conventional technique.

GPS is the most widely used global navigation satellite system. By design, there is no provision for real time integrity information within the Standard Positioning Service (SPS). However, in safety critical sectors like aviation, stringent integrity performance requirements must be met. This can be achieved externally or at the receiver level through receiver autonomous integrity monitoring (RAIM). The latter is a cost effective method that relies on data consistency, and therefore requires redundant measurements. An external aid to provide this redundancy can be in the form of an Inertial Navigation System (INS). This should enable continued performance even during RAIM holes (when no redundant satellite measurements are available). However, due to the inclusion of an additional system and the coupling mechanism, integrity issues become more challenging. To develop an effective integrity monitoring capability, a good understanding of the potential failure modes of the integrated system is vital. In this paper potential failure modes of integrated GPS/INS systems are identified. This is followed by the specification of corresponding models that would be required to investigate the capability of existing integrity algorithms and to develop enhancements or new algorithms.

Traditionally, on a great circle, the latitude or longitude of a waypoint is found by inspection. In this paper, using an elementary knowledge of vector algebra including linear combination of vectors and vector basis, we provide an easy method for finding the equation of a great circle path as a parameterized curve. By use of this vector function of distance travelled, the latitude and longitude of waypoints can be found based on the distance from departure point along a great circle. The approach is intended to appeal to the navigator who is interested in the mathematics of navigation and who, nowadays, solves his navigation problems with a personal computer.

We were amused by the Cross Track Distance at Mid-Longitude problem posed by Paul Hickley in The Journal of Navigation 57, 320. Two professors, John Ponsonby and Peter Hoare, replied to the invitations immediately. Both their solutions to the original article give superb accuracy. The two solutions are certainly ingenious and creative and encouraged us to develop new formula for building the Mid-Longitude Equation on great circle. Regrettably, the original author doesn’t think that the two were the solution that ATPL examiners were looking for. I also think that the two solutions would be beyond the capacity of the average undergraduate. Our method gives a good understanding logically and easily to be mnemonic, and the derivation process is found without any need to appeal to any formula of spherical trigonometry.