This paper describes the features of an extended Kalman filter algorithm designed to support the navigational function of a real-time vehicle performance and emissions monitoring system currently under development. The Kalman filter is used to process global positioning system (GPS) data enhanced with dead reckoning (DR) in an integrated mode, to provide continuous positioning in built-up areas. The dynamic model and filter algorithms are discussed in detail, followed by the findings based on computer simulations and a limited field trial carried out in the Greater London area. The results demonstrate that use of the extended Kalman filter algorithm enables the integrated system employing GPS and low cost DR devices to meet the required navigation performance of the device under development.

The Global Positioning System (GPS) is now attracting worldwide attention as a navigation sensor. One of the advantages of GPS is that people can find their position using a GPS receiver wherever they are except in obstructed environments. Existing GPS receivers do not work in an obstructed environment despite there being many potential applications. This paper shows the possibility of navigation using GPS technologies indoors in a blocked environment. The paper describes the pseudolite-based indoor navigation system that was developed and implemented at Seoul National University (SNU). The system, which uses GPS technologies, has some different characteristics to normal outdoor GPS systems. The differences cause some practical problems, such as near/far, time-tag, multipath and unknown transmission position problems, which need to be solved to implement the indoor navigation system. The paper introduces various methods for solving such problems. The paper then shows the experimental results and the system accuracy. The satisfactory experimental results show that the RMS static error is 1 mm(1σ) horizontally and 2 mm(1σ) vertically and the RMS dynamic error is 5·6 mm(1σ) horizontally and 15 mm(1σ) vertically. The paper also shows the result of a field application test in the ocean engineering basin of the Korea Research Institute of Ships & Ocean Engineering (KRISO).

This paper discusses the quantification of the current risk of mid-air collision to commercial air transport aircraft receiving a Radar Advisory Service in UK Class F/G airspace. The aim is to produce the best estimate, given published data, and then to indicate what extra studies and data would be needed to improve the estimate. A study of typical flight routeings by certain aircraft types, e.g. Sikorsky S76, Saab SF340, Short SD3-60 and Aeritalia ATR42-300, could provide useful additional information. The fitment and use of ACAS could be crucial in delivering the required protection against collision.

Navigating in unfamiliar road environments is a common and demanding cognitive activity. If this cannot be accomplished successfully, there are implications for increased driver workload, delays due to navigation errors, potentially unsafe road behaviour such as late lane changes, and inappropriate traffic management. To enable successful navigation through such environments, it is necessary to understand what the navigation task entails, and what a driver's corresponding information requirements are. This paper reports the results of a study that sought to identify what information is used by drivers when navigating within a range of urban driving environments, how this information is used, and what the implications are for the design of navigation aids.

This paper discusses the ship's navigation function, navigation processes, and navigational information. The relationship between these concepts and their substance are analysed in depth. However, the primary aim of this paper is to consider how the various types of navigational information and their content might be brought together in a logically defined structure as a Ship's Information System.

A geometric approach to Low Earth Orbiter (LEO) precision orbit determination (POD) based exclusively on triple-differenced GPS carrier phase observables is presented. The algorithm is fast and efficient because there is no ambiguity-resolution requirement, which is a complicated and time-consuming task for long-range kinematic GPS. However, the triple-difference kinematic LEO POD requires careful screening for cycle slips, as the orbit quality depends on the accuracy and the geometry of the observations. It is shown that the signal-to-noise-ratio (SNR) combined with residual screening offers a good approach to data cleaning. Data from a CHAMP (Challenging Minisatellite Payload, altitude ~450 km) mission are used to present the accuracy, speed and efficiency of the processing algorithms. The methods of data screening and the effects of geometry on the orbit quality are discussed and the final achievable accuracy including the limitations and benefits of this method are quantified. It is demonstrated that the triple-difference method offers short processing times and is currently capable of achieving a 3D RMS fit to the dynamic orbit of 15–30 cm and 1 mm/s to the velocity.

The neural networks (NN)-based geometry classification for good or acceptable navigation satellite subset selection is presented. The approach is based on classifying the values of satellite Geometry Dilution of Precision (GDOP) utilizing the classification-type NNs. Unlike some of the NNs that approximate the function, such as the back-propagation neural network (BPNN), the NNs here are employed as classifiers. Although BPNN can also be employed as a classifier, it takes a long training time. Two other methods that feature a fast learning speed will be implemented, including Optimal Interpolative (OI) Net and Probabilistic Neural Network (PNN). Simulation results from these three neural networks are presented. The classification performance and computational expense of neural network-based GDOP classification are explored.

To ensure high accuracy results from an integrated GPS/INS system, the carrier phase observables have to be used to update the filter's states. As a prerequisite the integer ambiguities must be resolved before using carrier phase measurements. However, a cycle slip that remains undetected (and uncorrected) will significantly degrade the filter's performance. In this paper, an algorithm that can effectively detect and identify any type of cycle slip is presented. The algorithm uses additional information provided by the INS, and applies a statistical technique known as the cumulative-sum (CUSUM) test. In this approach, cycle slip decision values can be computed from the INS-predicted position (due to the fact that its short-term accuracy is very high), and the CUSUM test used to detect cycle slips (as it is very sensitive to abrupt changes of mean values). Test results are presented to demonstrate the effectiveness of the proposed algorithm.

Accurate tide prediction is required for safe marine navigation in shallow waters as well as for other marine operations. Traditionally, tide prediction was carried out using the harmonic method, which is based on the identification of the harmonic tidal constituents existing in the tidal record. Unfortunately, however, unless long tidal records are available at the tide gauges, some important tidal constituents may not be identified. This, in turn, deteriorates the accuracy of the tidal prediction. More recently, a sequential least-squares prediction method capable of using relatively short tidal records was developed. This method allows for the modifications and corrections of the original solution of the tidal constituents when new observations and parameters are included. Although it reduces the computation time significantly compared to the batch harmonic method, this method exhibits large residuals particularly when very short tidal records are used. To overcome the limitations of the sequential least-squares method, a neural network-based model is developed for sequentially predicting the tidal heights using tide data series collected at various tide gauges. A modular, three-layer feedforward neural network trained using the back-propagation algorithm is used for this purpose. Tide data from three tide gauges are used to validate the model. A comparison is made between the developed neural network model and the sequential least squares method for tidal prediction. It is shown that the accuracy level of the tidal prediction has improved by a factor of 5 when using the neural network model.

A new navigation method, called a Line of Sight Counteraction Navigation (LOSCAN) algorithm has been introduced to aid manoeuvre decision making for collision avoidance based on a two-ship encounter. The LOSCAN algorithm is derived from an extension of the basic principle of traditional missile proportional navigation, recognising that the objective of the latter is target capture rather than target avoidance. The basic concept is to derive an acceleration command so as to increase the misalignment between the ships' relative velocity and the line-of-sight. The algorithm includes a risk assessment and the generation of appropriate navigation commands to manoeuvre own ship free of collision if a risk of collision exists. Numerical examples have been used to demonstrate the effectiveness of the algorithm. The relationship between the distance at the closest point of approach with respect to early warning distance, and with the norm of the acceleration, has also been analysed. In operation, the collision avoidance decision making process is a complicated problem, with its solution subject to ship states, practical dynamic constraints, Collision Avoidance Regulations (COLREGS), encountering ship manoeuvre coordination and human decision making factors. The proposed algorithm provides a consistent manoeuvre signal to aid decision-making.

In this study, an inverse Real Time Kinematic (RTK) GPS positioning approach is presented and discussed. GPS data from a remote receiver mounted on an unmanned vehicle is sent to the base station to determine the vehicle location at the centimetre level of accuracy in real time. Control of the vehicle's movement and manoeuvring can thus take place at the base station, which could be a few kilometres away, and the vehicle can be sent to specific locations to do certain tasks. Two GPS antennas were used in order to increase positioning reliability and estimate real time heading and pitch of the vehicle for better control. In addition, the vehicle was fitted with cameras with wireless video transmission to provide the operator with a good level of vision in different directions during operation. The main applications of such a system would be in exploration, breaching and clearance of minefields, and hazardous situations such as fire fighting of burning oil wells. The system design and techniques employed are discussed first. Issues addressed include antenna layout, data transmission, solution algorithm, and ambiguity resolution. The proposed system was mounted on a prototype vehicle and tested under different satellite visibility conditions. Results show that high positioning accuracy with reliable ambiguity resolution can be obtained with the developed approach if the number of observed satellites is 5 or more and PDOP is less than 5. Heading and pitch were determined within 0·2–0·3 degree using a 1·2 m long baseline. This performance can be improved as the length of the on-board baseline increases.

This paper shows a possible new use of a common or computerised star finder for a rapid and easy solution to some space problems such as:

1. – launch windows;

2. – satellite fly-over;

3. – the rise and setting of a satellite;

4. – Earth or Moon satellite surveys;

5. – the eclipse of a satellite.

Transfer alignment is the process of initialising and calibrating a weapon INS using data from the host aircraft's navigation system. To determine which transfer alignment technique performs best, different design options have been assessed, supported by simulation work. The dependence of transfer alignment performance on environmental factors, such as manoeuvres, alignment duration, lever arm and inertial sensor quality has also been studied. ‘Rapid’ alignment, using attitude as well as velocity measurements was found to perform better than ‘conventional’ techniques using only velocity. Innovative developments include the estimation of additional acceleration and gyro states and estimation of force dependent relative orientation, which has enabled robust alignment using wing rock manoeuvres, which do not require the pilot to change trajectory. Transfer alignment has been verified in real-time by flight trials on a Tornado aircraft. In addition, techniques have been developed to prevent transients in the aircraft integrated navigation solution following GPS re-acquisition after an outage of several minutes from disrupting the transfer alignment process.

In Satellite-Based Augmentation Systems (SBAS), the correction messages are transmitted to the users' receivers via geostationary communication satellites (GEOS) at GPS L1 (1575·42 MHz) frequency. Severe scintillations in the equatorial zone disrupt geostationary satellite links even at L-band. Observations of scintillations at 1·5 GHz from Calcutta (22·58°N, 88·38°E geographic, 32°N magnetic dip), located near the crest of the equatorial anomaly in the Indian zone, show that scintillations occur in patches of duration varying from a few minutes to several hours. During the solar maximum years 1998–2000, severe scintillations (Scintillation Index [ges ]15 dB) were recorded for 48 hr 55 min (1·27%) out of the total observation time of 3868 hr 9 min in the local time interval 19 to 00 hrs. In order to have a fail-safe system, it is suggested that more than one geostationary satellite be used in SBAS so that, if one link is disrupted, the other can be used for transmission of correction messages to the GPS users. The minimum longitudinal separation between two GEOS required for reliable operation of SBAS has been estimated, from the cumulative distribution of scintillation patch duration, to be 57° in the Indian longitude zone.

GPS and low-cost INS sensors are widely used for positioning and attitude determination applications. Low-cost inertial sensors exhibit large errors that can be compensated using position and velocity updates from GPS. Combining both sensors using a Kalman filter provides high-accuracy, real-time navigation. A conventional Kalman filter relies on the correct definition of the measurement and process noise matrices, which are generally defined a priori and remain fixed throughout the processing run. Adaptive Kalman filtering techniques use the residual sequences to adapt the stochastic properties of the filter on line to correspond to the temporal dependence of the errors involved. This paper examines the use of three adaptive filtering techniques. These are artificially scaling the predicted Kalman filter covariance, the Adaptive Kalman Filter and Multiple Model Adaptive Estimation. The algorithms are tested with the GPS and inertial data simulation software. A trajectory taken from a real marine trial is used to test the dynamic alignment of the inertial sensor errors. Results show that on line estimation of the stochastic properties of the inertial system can significantly improve the speed of the dynamic alignment and potentially improve the overall navigation accuracy and integrity.