A relatively novel approach of autonomous navigation employing platform dynamics as the primary process model raises new implementational challenges. These are related to: (i) potential numerical instabilities during longer flights; (ii) the quality of model self-calibration and its applicability to different flights; (iii) the establishment of a global estimation methodology when handling different initialisation flight phases; and (iv) the possibility of reducing computational load through model simplification. We propose a unified strategy for handling different flight phases with a combination of factorisation and a partial Schmidt–Kalman approach. We then investigate the stability of the in-air initialisation and the suitability of reusing pre-calibrated model parameters with their correlations. Without GNSS updates, we suggest setting a subset of the state vector as ‘considered’ states within the filter to remove their estimation from the remaining observations. We support all propositions with new empirical evidence: first in model-parameter self-calibration via optimal smoothing and second through applying our methods on three test flights with dissimilar durations and geometries. Our experiments demonstrate a significant improvement in autonomous navigation quality for twelve different scenarios.

Autonomous vehicles rely on a combination of sensors for safe navigation around the world. For precise localisation, high-definition (HD) maps are used. These maps are a representation of the world containing information about objects on the road infrastructure. Currently, there are tens of HD map makers, however, no rigorous description of the requirements for the accuracy of HD maps has been published yet. This study fills the gap and offers a mathematical description of the minimum required accuracy for HD maps. In the first part, we identify factors that influence the quality of a map. Based on that, we proceed to present our solution for determining the minimum required accuracy for HD maps, both for static and dynamic models, and present a new formula for the minimum necessary accuracy for HD maps.

The possibility of identifying the type of multipath environment and receiver motion (e.g. pedestrian, vehicular) using pattern recognition approaches based on multipath parameters is investigated. This allows the receiver to adjust its tracking strategy and optimally tune its tracking parameters to mitigate code multipath effects. A Support Vector Machine (SVM) classification method with a modified Gaussian kernel is applied in this approach. A set of temporal and spectral features is extracted from the correlation samples of the received signals in different environments to train the classifier. The latter is then used in the structure of stochastic gradient-based adaptive multipath compensation and tracking techniques to tune the signal tracking parameters based on the environment and receiver motion. Simulation and real data measurements using Galileo E1B/C signals are performed to assess the validity of the proposed environment identification approaches and to evaluate the impact of the proposed context-based receiver parameter tuning techniques on tracking performance in multipath environments. Test results showed that the proposed classifiers have an accuracy between 86% and 92%, and the tracking performance improved by about 15%.

Recent navigation systems integrating GPS with Micro-Electro-Mechanical Systems (MEMS) Inertial Measuring Units (IMUs) have shown promising results for several applications based on low-cost devices such as vehicular and personal navigation. However, as a trend in the navigation market, some applications require further reductions in size and cost. To meet such requirements, a MEMS full IMU configuration (three gyros and three accelerometers) may be simplified. In this context, different partial IMU configurations such as one gyro plus three accelerometers or one gyro plus two accelerometers could be investigated. The main challenge in this case is to develop a specific navigation algorithm for each configuration since this is a time-consuming and costly task. In this paper, a universal approach for processing any MEMS sensor configuration for land vehicular navigation is introduced. The proposed method is based on the assumption that the omitted sensors provide relatively less navigation information and hence, their output can be replaced by pseudo constant signals plus noise. Using standard IMU/GPS navigation algorithms, signals from existing sensors and pseudo signals for the omitted sensors are processed as a full IMU. The proposed approach is tested using land-vehicle MEMS/GPS data and implemented with different sensor configurations. Compared to the full IMU case, the results indicate the differences are within the expected levels and that the accuracy obtained meets the requirements of several land-vehicle applications.

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.

The increasing volume of traffic in urban areas has resulted in steady growth of the mean driving time on fixed routes. Longer driving times lead to significantly higher transportation costs, particularly for vehicle fleets, where efficiency in the distribution of their transport tasks is important in staying competitive in the market. For bus fleets, the optimal control and command of the vehicles is, as well as the economic requirements, a basic function of their general mission. The Global Positioning System (GPS) allows reliable and accurate positioning of public transport vehicles except within the physical limitations imposed by built-up city ‘urban canyons’. With a view to the next generation of satellite positioning systems for public transport fleet management, this paper highlights the limitations imposed on current GPS systems operating in the urban canyon. The capabilities of a future positioning system operating in this type of environment are discussed. It is suggested that such a system could comprise receivers capable of integrating the Global Positioning System (GPS) and the Russian equivalent, the Global Navigation Satellite System (GLONASS), and relatively cheap dead-reckoning sensors.