Simulations suggest that navigation software may not be as efficient as expected for city traffic.

The aim of the paper is to investigate, design and construct the Global Positioning System (GPS) navigation system, communications bus, and central computer for an autonomous ocean vessel. The objectives were: to interface a GPS receiver to a host processor; to design a communications bus connecting the central computer to the host processor; and to design the central computer. The software to control the GPS receiver remotely from the central computer has been completed. The host processor is now able to request the GPS receiver for any message, and is able to command the receiver to perform different tasks and change current settings. Buffers have been incorporated into the code to increase efficiency. The host processor can now transmit to and receive from the central computer across the communications network. A marine navigational bus standard, NMEA 0183, has been researched and found suitable for this application. Interfaces to the bus have been constructed and tested. The functionality of the central computer has been implemented on an IBM-PC compatible, and is able to control the host processor. This paper aims to inform of the results obtained in the above mentioned areas.

Neural networks have been proposed as nonlinear filters in a variety of applications that involve nonlinear processing of input signals; examples include blind signal separation, image registration, and blind deconvolution. The Global Positioning System (GPS) navigation equations are nonlinear (quadratic) in nature, and a direct closed form solution of the GPS navigation equations does not exist. This article presents a new approach to solving the GPS pseudorange equations using three-layer neural networks. A three-layer radial basis function (RBF) neural network is designed, which solves the non-linear GPS pseudorange equations directly as opposed to the linear least squares or extended Kalman filter approaches in traditional GPS receivers. For training the neural network, a carefully selected cost function is minimized using a variation of the classical conjugate gradient algorithm such that training time for the neural network is reasonable. Simulations have been performed at SiRF Technology Inc. that show stable behavior even under bad geometry conditions where the traditional recursive least squares and extended Kalman filter approaches show high sensitivity to measurement errors. Under good geometry conditions the neural network solution shows slightly improved noise performance compared to the expected performance of traditional leas squares solution. Simulations have been performed with additive white Gaussian noise and correlated noise models to evaluate the performance of the trained neural network. © 2000 John Wiley & Sons, Inc.

We study the impact of deserting a pre-established path, determined by a navigation software, on the overall city traffic. To do so, we consider a cellular automaton model for vehicular traffic, where the cars travel between two randomly assigned points in the city following three different navigation strategies based on the minimization of the individual paths or travel times. We found, in general, that, above a critical car density, the transport improves in all strategies if we decrease the time that the vehicles persist in trying to follow a particular strategy when a route is blocked, namely, the mean flux increases, the individual travel times decrease, and the fluctuations of density in the streets decrease; consequently, deserting helps prevent traffic jams.

In this study, the integration of two navigation systems Air Data System (ADS) and Global Positioning System (GPS) was aimed. ADS is a widely used navigation system which measures static and total air pressure and the air temperature. ADS has high sampling frequency and poor accuracy, on the other hand, another navigation system GPS has high accuracy compared to ADS but lower sampling frequency.Kalman Filter is used to integrate and minimize the errors of the two navigation systems. By this integration a navigation system with high sampling frequency and high accuracy is aimed. Another object is to calculate the wind speed with high accuracy.

While GPS (Global Positioning System) navigation and GPS based autonomous car driving techniques are mature, the security of GPS signal has not been a primary system concern. In fact, it has been shown that an ordinary GPS can be easily spoofed by a low-cost, open-source based software defined radio (SDR) system such as bladeRF and HackRF [3, 4] which can cause serious complications to the navigation system of the car especially for self-driving cars which are driven based on the information from sensors, cameras, and GPS. In this work, we design a new GPS authentication system called lightweight authentication GPS (AuthGPS) to authenticate GPS signal against GPS spoofing and LTE base station broadcast message spoofing. In the past, there have been many successful attempts on GPS spoofing which resulted in shifting the destination of the car to the spoofer's desired location. In a case where the car is connected to the Internet via LTE network, the navigation system can find out if the GPS is spoofed by acquiring the correct GPS satellite information from LTE base stations' location information. However, a skillful attacker can spoof the LTE base station signals [7] as well using another SDR which will produce spoofed LTE base station information. Hence giving wrong information about GPSinformation according to the will of the attacker. Currently, there are defense methods against the GPS spoofing [6]. One of them is signal-processing-based methods. By monitoring unusual or unreasonable signal changes at GPS receivers, GPS spoofing attack can be detected. Received Power Monitoring (RPM) looks at all the received amplitude and automatic gain control (AGC) setpoint [1]. The receiver will sense drastic power jump if a spoofing attack occurs. However, an overly powerful spoofing attack with noise is not detectable in the case of this mechanism. Another way to detect spoofing is the symmetric-key encryption mechanism of GPS signals. According to previous research [2], encrypted precision code for anti-spoofing referred to as P(Y) code, might not be able to be spoofed. A spoofing attack can be detected by calculating cross-correlation between spoofed coarse/acquisition code, referred to as C/A code, and P(Y) code. Unfortunately, this P(Y) code is only for military purpose while the C/A code can be publicly accessible. This method requires knowledge of specific key which is not revealed to the public to decrypt P(Y) code. Monitoring the direction of arrival of the signals can be one of the methods [6]. A GPS receiver measures the direction-of-arrival vector with more than 3 antennas. Even though this signal-geometrybased system with multiple antennas makes GPS robust, an attacker can spoof signals from multiple directions, which is more difficult to detect the spoofing. The idea here is to develop a system that can discriminate the spoofed signal without complex computation or heavy message exchange. An important component of AuthGPS is a verification of valid GPS satellite information via LTE base stations' location information. We propose 6-digit one-time password-based authentication system in this paper.

This paper is to give the outline in the design, development and test software to study the rate of climb for global positioning system (GPS) paraglide. It covers the assembling of the model paraglide, the interfacing of a GPS and inertia navigation system (INS). The memory map and the flow charts of the guidance controller (GC) are given and commented. Theoretical background is also included.

Global Positioning System (GPS) data for phase delay differential between Ll and L2 frequencies are analyzed by a model in which the combined effect of hardware and phase ambiguity terms is represented by a constant for each receiver‐satellite path and the ionospheric contribution is represented by the RIBG model (ray trace through the combined ICED (ionospheric conductivity and electron density), Bent, and Gallagher ionospheric models). RIBG contains a detailed global empirical climatological ionospheric model of the electron density versus height profile (EDP) from 80 km up to the plasmapause, which is combined with a full three‐dimensional ray trace propagation model. Discrete inverse theory (DIT) is used to fit the driving parameters of RIBG to about 2 hours worth of data from a single GPS receiver at various geographical locations. Validity is demonstrated by the ability of the GPS‐updated RIBG model to predict independent measurements of (1) vertical total electron content (TEC) over the ocean out to substantial distances from a GPS receiver, often in excess of 2000 km at midlatitudes, and (2) the electron density versus height profile (EDP). Performance variation with magnetic latitude is noted. Flexibility in RIBG to adjust different driving parameters for different parts of the EDP, in order to obtain a best fit of GPS data, benefits the EDP prediction. Implications of this approach for GPS navigation and other radio system applications are discussed.

Mobile robot has been a major role to the application in military, industrial and agricultural purposes. Mobile robot should navigate through desire route and avoid the obstacle within the path. Many researcher come with the solution by using the various type of control and instrumentation system. The complexity of mobile robot system can make the system cost intensive and high risk. Proposed is a mobile robot equipped with GPS navigation system and obstacle avoidance system with low cost mobile structure, GPS module and sonar sensor. The combination GPS and Sonar will determine the position and obstacle avoidance for the mobile robot. Mobile robot should navigate according to waypoint that preset to the GPS module and sonar sensor detects the obstacle during mobile robot navigation by triggering the sonar sensor in sequence by using commanded loop daisy chaining application method. Mobile Robot can navigate through desired waypoint and at the same time apply the obstacle avoidance rules

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) are very practical method to get high precision navigation. GPS/INS data Fusion in navigation has been a research topic for decades. Usually the update time of GPS an INS is different. The update time of GPS is 20HZ, and the time of INS is 100HZ or higher. Since GPS and INS are mainly methods in navigation system. Techniques of fusing GPS and INS data are in high demand in navigation industry. Usually the Karman filter is used to fuse the data from GPS and INS. But the Kalman filter is model-dependent and a priori the model parameters need to be known. In this work, a generic and intelligent approach of GPS and INS data fuse using fuzzy interpolation before Kalman algorithm is proposed. First a simplified interpolation model is introduced in a ordinary GPS and INS data fusion. Second a fuzzy rule-based system is established to fuzzy interpolation with objective fuzzy method. Then the parameters of objective fuzzy system are identified, the best n-rule is determinate and the optimal rule base system is found. Finally this approach is applied on an example for a car GPS and INS data fusion, and the results are calculated with computer system to demonstrate the advantages of this approach.

Global Positioning System and Inertial Navigation System can be used to determine position and velocity. A Global Positioning System module is able to accurately determine position without sensor drift, but its usage is limited in heavily urbanized environments and heavy vegetation. While high-cost tactical-grade Inertial Navigation System can determine position accurately, low-cost micro-electro-mechanical system Inertial Navigation System sensors are plagued by significant errors. Global Positioning System is coupled with Inertial Navigation System to correct the errors, while Inertial Navigation System itself can be used to provide navigation solution during a Global Positioning System outage. Data from Global Positioning System and Inertial Navigation System can be integrated by extensive Kalman filtering, using loosely coupled integration architecture to provide navigation solutions. In this study, real-time low-cost loosely coupled micro-electro-mechanical system Inertial Navigation System/Global Positioning System sensors have been used for pedestrian navigation. Trial runs of Global Positioning System outages have been conducted to determine the accuracy of the system described. The micro-electro-mechanical system Inertial Navigation System/Global Positioning System can successfully project a trajectory during a Global Positioning System outage and produces a root mean square error of 9.35 m in latitude direction and 10.8 m in longitude direction. This technology is very suitable for visually impaired pedestrians.

Divided difference filter (DDF) with quaternion-based dynamic process modeling is applied to global positioning system (GPS) navigation. Using techniques similar to those of the unscented Kalman filter (UKF), the DDF uses divided difference approximations of derivatives based on Stirling’s interpolation formula which results in a similar mean but different posterior covariance compared to the extended Kalman filter (EKF) solutions. The second-order divided difference is obtained from the mean and covariance in second-order polynomial approximation. The quaternion-based dynamic model is adopted for avoiding the singularity problems encountered in the Euler angle method and enhancing the computational efficiency. The proposed method is applied to GPS navigation to increase the navigation estimation accuracy at high-dynamic regions while preserving (without sacrificing) the precision at low-dynamic regions. For the illustrated example, the second-order DDF can deliver about 41–82% accuracy improvement as compared to the EKF. Some properties and performance are assessed and compared to those of the EKF and UKF approaches.

In recent years, a number of studies have been done on GPS (Global Positioning System) due to the wide applications. One important research issue is on the GPS navigation. In this paper, we propose a novel data mining algorithm named PATE (prediction-based algorithm for travel time evaluation) that can efficiently predict the travel time of a navigation path and precisely recommends the navigation path to the users under a user-specified travel time constraint in road network environments. To our best knowledge, this is the first work on discovering the shortest navigation path within a travel time constraint. Furthermore, we propose a novel search structure named NPST (navigation path search tree) for efficiently finding the shortest navigation path that meets the user-specified travel time constraint. Through a series of experiments, the proposed method was shown to have excellent performance under different system conditions.

With the development of the vehicle industry, controlling stability has become more and more important. Techniques of evaluating vehicle stability are in high demand. Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) is a very practical method to get high-precision measurement data. Usually, the Kalman filter is used to fuse the data from GPS and INS. In this paper, a robust method is used to measure vehicle sideslip angle and yaw rate, which are two important parameters for vehicle stability. First, a four-wheel vehicle dynamic model is introduced, based on sideslip angle and yaw rate. Second, a double level Kalman filter is established to fuse the data from Global Positioning System and Inertial Navigation System. Then, this method is simulated on a sample vehicle, using Carsim software to test the sideslip angle and yaw rate. Finally, a real experiment is made to verify the advantage of this approach. The experimental results showed the merits of this method of measurement and estimation, and the approach can meet the design requirements of the vehicle stability controller.

Determining the correct height of mountain peaks is vital for tourism, but it is also important as a reference point for devices equipped with GPS (Global Positioning System), e.g., watches or car navigation systems. The peak altitude data are part of geographic and geodetic information. As more modern technologies and equipment become available, their precisions should increase. However, verification of peak heights is usually only conducted for the highest, well-known mountains—lower peaks or mountain passes are rarely verified. Therefore, this study focuses on an investigation of 12 altitude points on a section of the longest and most famous touristic trail in Poland (the Main Beskid Trail), located in the Orava–Żywiec Beskids Mts (Mountains). The aim of this research is to measure and verify the heights of the 12 selected mountain peaks, in addition to evaluating the chosen methods based on the quality of the obtained data and determining their suitability and opportunities for use in further research. Measurements were obtained at the most specific height points—on the 12 highest points of the summits. This study compares two modern measurement techniques: the global navigation satellite system (GNSS) and light detection and ranging (LiDAR). The obtained results were later compared with those widely used on the internet and in printed materials (period covered: 1884–2015). This analysis demonstrates that lesser-known objects are rarely the subject of remeasurement and significant altitude errors may occur, primarily because the heights originated from a source in the past when modern methods were not available. Our findings indicate that the heights of the peaks presented in cartographic materials are inaccurate. The assumed heights should be corrected by direct measurements using modern techniques.

To address the economic feasibility of Auto-Guidance and lightbar global positioning system (GPS) navigation technologies, a linear programming model was formulated using data from U.S. Corn Belt farms. Five scenarios were compared: (i) a baseline scenario with foam, disk or other visual marker reference, (ii) lightbar navigation with basic GPS availability (+/-3 dm accuracy), (iii) lightbar with satellite subscription correction GPS (+/-1 dm), (iv) auto-guidance with satellite subscription (+/-1 dm), and (v) auto-guidance with a base station real time kinematic (RTK) GPS (+/-1 cm). Returns from each scenario were compared over incremental management scenarios.