The use of low-cost GPS receivers and antennas for attitude determination can significantly reduce the overall hardware system cost. Compared to the use of high performance GPS receivers, the carrier phase measurements from low-cost equipment are subject to additional carrier phase measurement errors, such as multipath, antenna phase centre variation and noise. These error sources, together with more frequent cycle slip occurrences, severely deteriorate attitude determination availability, reliability and accuracy performance. This paper presents the investigation of a low-cost GPS/gyro integration system for attitude determination. By employing the dead reckoning sensor type, the ambiguity search region can be specifically defined as a small cube to enhance the ambiguity resolution process. A Kalman filter is implemented to fuse the rate gyro data with GPS carrier phase measurements. The quality control system based on innovation sequences is used to identify cycle slip occurrences and incorrect inter-antenna vector solutions. The availability of the integrated system also improves with respect to the GPS standalone system since the attitude parameters can be estimated using the angular rate measurements from rate gyros during GPS outages. The low-cost hardware used to design and test the integrated system consists of CMC Allstar receivers with the OEM AT575-70 antennas and Murata ENV-05D-52 piezoelectric vibrating rate gyroscopes. Tests in the urban area demonstrated that the introduction of rate gyros in a GPS-based attitude determination system not only effectively decreased the noise level in the estimated attitude parameters but coasted the attitude output during GPS outages and also significantly improved the system reliability.

We propose a robust trajectory planning method for vision-based autonomous land vehicle (ALV) guidance by target following using a visual field model. The proposed trajectory planning method aims to guide the ALV to achieve the goal that the targeted person will always appear in the image. It is found in this study that the goal can be achieved if three visual contact constraints in the visual field model are satisfied. The first constraint postulates that the target has appeared in the image. The second constraint asks that the vehicle head at the next position points straight towards to the target's current position. The third constraint sets bounds between the next position of the ALV and the current position of the target. A formula for the trajectory of the vehicle that satisfies the second visual contact constraint is derived. The steps for generating a speed and a turn angle of the ALV for real-time navigation are described as a trajectory-planning algorithm. Finally, the approach is tested on a real ALV and successful navigation sessions confirm the feasibility and the robustness of the approach.

GPS-based land vehicle navigation systems are subject to signal fading in urban areas and require aid from other enabling sensors. A low-cost gyro-free inertial navigation system (INS) without accumulated attitude errors and complicated initializations could be an effective solution to the problem. This paper investigates a Constrained Navigation Algorithm (CNA) and the Artificial Neural Network (ANN) technique to compensate velocity output from a gyro-free INS. The vehicle's heading will be calibrated by a full circle test so that the magnetometer's bias and scale factor error could be removed. Experiments with a vehicle driven over level terrain have been conducted to assess the performance of the compensated gyro-free INS solutions. The effect of the architecture of Neural Network on prediction performance has also been discussed as well as the applicability of the proposed solution to land vehicle navigation with GPS outages.

Attitude estimation systems often use two or more different sensors to increase reliability and accuracy. Although gyroscopes do not have problems like limited range, interference, and line of sight obscuration, they suffer from slow drift. On the other hand, inclinometers are drift-free but they are sensitive to transverse accelerations and have slow dynamics. This paper presents an extended Kalman filter (EKF)-based data fusion algorithm which utilizes the complementary noise profiles of these two types of sensors to extend their limits. To avoid complexities of dynamic modelling of the platform and its interaction with the environment, gyro modelling will be used to implement indirect (error state) form of the Kalman filter. The great advantage of this approach is its independence from the structure of the platform and its applicability to any system with a similar set of sensors. Separate bias formulation of the Kalman filter will be used to reduce the computational complexity of the algorithm. In addition, a systematic approach based on wavelet decomposition will be utilized to estimate noise covariances used in the Kalman filter formulation. This approach solves many of the convergence problems encountered in the implementation of EKF due to the choice of covariance matrices. Experimental implementation of the estimator shows the excellent performance of the filter.

In this paper, the Auto-Regressive Moving-Averaging (ARMA) neural networks (NNs) will be incorporated for predicting the differential Global Positioning System (DGPS) pseudorange correction (PRC) information. The neural network is employed to realize the time-varying ARMA implementation. Online training for real-time prediction of the PRC enhances the continuity of service on the differential correction signals and therefore improves the positioning accuracy. When the PRC signal is lost, the ARMA neural network predicted PRC would temporarily provide correction data with very good accuracy. Simulation is conducted for evaluating the ARMA NN based DGPS PRC prediction accuracy. A comparative performance study based on two types of ARMA neural networks, i.e. Back-propagation Neural Network (BPNN) and General Regression Neural Network (GRNN), will be provided.

The Global Positioning System (GPS) can be employed as a free attitude determination interferometer when carrier phase measurements are utilized. Conventional approaches for the baseline vectors are essentially based on the least-squares or Kalman filtering methods. The raw attitude solutions are inherently noisy if the solutions of baseline vectors are obtained based on the least-squares method. The Kalman filter attempts to minimize the error variance of the estimation errors and will provide the optimal result while it is required that the complete a priori knowledge of both the process noise and measurement noise covariance matrices are available. In this article, a neural network state estimator, which replaces the Kalman filter, will be incorporated into the attitude determination mechanism for estimating the attitude angles from the noisy raw attitude solutions. Employing the neural network estimator improves robustness compared to the Kalman filtering method when uncertainty in noise statistical knowledge exists. Simulation is conducted and a comparative evaluation based on the neural network estimator and Kalman filter is provided.

Accurate vehicle location is essential for various applications in the field of intelligent transportation systems (ITS). Existing vehicle location systems rely on multiple positioning sensors and powerful computing devices to execute complex map matching algorithms. There exists a strong need for exploring a solution for vehicle location that relies on a GPS receiver as the sole means of positioning and does not require complex computations. Towards this end, the error characteristics of the GPS signal were studied through the analysis of GPS data collected during test drives. Based on the inferences drawn and a simple fuzzy rule set, a novel yet simple map matching algorithm was developed. Due to the difficulties in testing the algorithm through on-road trials, a simulation environment that is capable of reproducing the field conditions in the laboratory was developed. Simulation results confirm that the proposed algorithm overcomes many of the inadequacies of the existing methods and is capable of achieving high accuracy with minimal computational requirements.

The accuracy that can be achieved by a single GPS receiver in stand-alone mode is around 10 metres with SA switched off; this is not adequate for most hydrographic studies. This study aims to improve this level of accuracy using a single geodetic receiver in stand-alone mode by applying a phase smoothing algorithm. The algorithm described in this study requires that the measurements be started from a known point for initialisation. The test site selected for the study is Halic bay, Istanbul. After GPS data were collected on a geodetic point for initialisation, the receiver was moved to a hydrographic vessel and trial measurements were performed along several survey profiles. The position of the vessel was calculated at every epoch using the carrier phase smoothing algorithm. In order to analyse the accuracy of the results, a second receiver collected data on another known point on the seashore during the whole session. The position of the vessel was accurately determined using a kinematic positioning method considering the data collected by both receivers. The results produced show that an accuracy of less than a metre can be achieved using the approach considered in this study.

Recent studies confirm the importance of satellite positioning in location-based services (LBS) development. A field study was conducted in suburban and rural areas near Zagreb, Croatia in order to examine the real-time data compliance with recently established positioning performance requirements for LBS quality of service (QoS). Data analysis was based on comparison between actual positioning performance and pre-specified positioning parameter values using defined comparative procedures. The results presented here confirm a good correlation between the actual and required positioning performance, even without implementation of any of augmentation or assistance positioning methods.

An adaptive integrated Kalman filtering based on the adjustment outputs of local navigation sensors and the outputs of a dynamic or kinematic state model is presented, which avoids the correlations of the local Kalman filtering outputs affected by the same disturbances of the dynamic state model. It has the advantage of rigor in theory and simple in calculation as well as adaptive in the various local navigation outputs. An integrated navigation estimator that is similar to the federated Kalman filtering is given as an initial estimate of a state based on the information sharing principle, but without any dynamic model information. An adaptive integrated fusion of the local navigation outputs and the dynamic model information is followed, in which the weights of the local navigation outputs and the dynamic model outputs are determined based on their differences from the integrated navigation results. The processing algorithms, logic, and associated computer burden are similar to those of federated filter. A simulated example is given to show the effectiveness of the new adaptive integrated navigation algorithm.

The extended Kalman filter, when employed in the GPS receiver as the navigation state estimator, provides optimal solutions if the noise statistics for the measurement and system are completely known. In practice, the noise varies with time, which results in performance degradation. The covariance matching method is a conventional adaptive approach for estimation of noise covariance matrices. The technique attempts to make the actual filter residuals consistent with their theoretical covariance. However, this innovation-based adaptive estimation shows very noisy results if the window size is small. To resolve the problem, a multilayered neural network is trained to identify the measurement noise covariance matrix, in which the back-propagation algorithm is employed to iteratively adjust the link weights using the steepest descent technique. Numerical simulations show that based on the proposed approach the adaptation performance is substantially enhanced and the positioning accuracy is substantially improved.

We propose in this paper a method to enhance the performance of a coupled global positioning/inertial navigation system (GPS/INS) for land navigation applications during GPS signal loss. Our method is based on the use of an artificial neural network (ANN) to intelligently aid the GPS/INS coupled navigation system in the absence of GPS signals. The proposed enhanced GPS/INS is tested in the dynamic environment of a land vehicle navigating around a closed path on the METU campus and we provide the results. Our GPS/INS+ANN system performance is thus demonstrated with a land trial.

Clod Shannon's fundamental works in the field of Theory of Information are well known and used in many different fields of science and practice. The Theory provides good criteria to evaluate equipment, systems, different effects and situations from the point of view of the necessary information. This article uses the Theory to attempt to create an information model for a “Ship-Danger to Navigation” system, which can evaluate an Integrated Bridge System in critical situations.

This paper investigates an RTK-GPS correction generation technique suitable for low-rate (<600 bps) data-link. We combined correction filtering algorithm with an efficient transmission technique which is based upon correction scheduling. The correction scheduling technique can reduce the required amount of correction data as well as the latency effect on RTK-GPS positioning accuracy. We processed real time GPS data to evaluate the performance of the RTK-GPS correction generator for low-rate data-link. The experimental results demonstrate that the proposed techniques can lower the required bandwidth of data-link up to 500 bps, maintaining the high accuracy of RTK-GPS.

GMDSS alerting was introduced in order to automate the call for search and rescue of a ship in distress, through the utilization of the most modern terrestrial and celestial communication systems. However, the GMDSS suffers from the inefficiencies associated with equipment design, operator competence and, most importantly, with those relevant to the equipment–operator interface. The GMDSS inefficiencies are frequently manifested in the form of false alerts, which apart from using resources, degrade the overall effectiveness of the SAR operation. In this paper, the GMDSS post-full-implementation record of false alerts received by the Greek MRCC is presented and analyzed. The observed persistence of high incidence of false alerts dictates the need for the proposal of further measures to combat false alerts, to promote the intended mission of the system and to generally improve the safety of navigation at sea.

Ari sailed from Ireland to the American continent, but in how many days? This is an extract of a lecture held in Roskilde, Denmark, for an assembly of persons with an interest and knowledge of maritime history.