Journal of Global Positioning Systems 

About: Journal of Global Positioning Systems is an academic journal. The journal publishes majorly in the area(s): Global Positioning System & GNSS applications. Over the lifetime, 258 publications have been published receiving 5568 citations.

A Step, Stride and Heading Determination for the Pedestrian Navigation System

Abstract: Recently, several simple and cost-effective pedestrian navigation systems (PNS) have been introduced. These systems utilized accelerometers and gyros in order to determine step, stride and heading. The performance of the PNS depends on not only the accuracy of the sensors but also the measurement processing methods. In most PNS, a vertical impact is measured to detect a step. A step is counted when the measured vertical impact is larger than the given threshold. The numbers of steps are miscounted sometimes since the vertical impacts are not correctly measured due to inclination of the foot. Because the stride is not constant and changes with speed, the step length parameter must be determined continuously during the walk in order to get the accurate travelled distance. Also, to get the accurate heading, it is required to overcome drawbacks of low grade gyro and magnetic compass. This paper proposes new step, stride and heading determination methods for the pedestrian navigation system: A new reliable step determination method based on pattern recognition is proposed from the analysis of the vertical and horizontal acceleration of the foot during one step of the walking. A simple and robust stride determination method is also obtained by analyzing the relationship between stride, step period and acceleration. Furthermore, a new integration method of gyroscope and magnetic compass gives a reliable heading. The walking test is preformed using the implemented system consists of a 1-axis accelerometer, a 1-axis gyroscope, a magnetic compass and 16-bit microprocessor. The results of walking test confirmed the proposed method.

Virtual Reference Station Systems

Abstract: Dr. Herbert Landau, is Managing Director of Trimble Terrasat and Director of GPS Algorithms and Infrastructure software in Trimble's G&E Division. He received his Ph.D. in Geodesy from the University FAF Munich, Germany in 1988. He has many years of experience in GPS and has been involved in a large variety of GPS and GLONASS developments for high precision positioning systems and applications. Dr. Ulrich Vollath, received a Ph.D. in Computer Science from the Munich University of Technology (TUM) in 1993. He is Senior Manager of the department of kinematic positioning and real-time systems of Trimble Terrasat. Dr. Xiaoming Chen, is a software engineer at Trimble Terrasat. He holds a PhD in Geodesy from Wuhan Technical University of Surveying and Mapping.

Evaluating the Performances of Adaptive Kalman Filter Methods in GPS/INS Integration

Abstract: One of the most important tasks in integration of GPS/INS is to choose the realistic dynamic model covariance matrix Q and measurement noise covariance matrix R for use in the Kalman filter. The performance of the methods to estimate both of these matrices depends entirely on the minimization of dynamic and measurement update errors that lead the filter to converge. This paper evaluates the performances of adaptive Kalman filter methods with different adaptations. Innovation and residual based adaptive Kalman filters were employed for adapting R and Q. These methods were implemented in a loose GPS/INS integration system and tested using real data sets. Their performances have been evaluated and compared. Their limitations in real-life engineering applications are discussed.

Adaptive Kalman Filtering for Vehicle Navigation

Abstract: Kalman filters have been widely used for navigation and system integration. One of the key problems associated with Kalman filters is how to assign suitable statistical properties to both the dynamic and the observational models. For GPS navigation, the manoeuvre of the vehicle and the level of measurement noise are environmental dependent, and hardly to be predicted. Therefore to assign constant noise levels for such applications is not realistic. In this paper, real-time adaptive algorithms are applied to GPS data processing. Two different adaptive algorithms are discussed in the paper. A number of tests have been carried out to compare the performance of the adaptive algorithms with a conventional Kalman filter for vehicle navigation. The test results demonstrate that the new adaptive algorithms are much robust to the sudden changes of vehicle motion and measurement errors.

Using RFID for Accurate Positioning

Abstract: In Korea, telematics is regarded as the technology to enhance and make everyday-driving experience more comfortable and safer. An essential part of the telematics is navigation and it is mainly based on GPS as the choice of positioning technology. The accuracy of GPS, however, is approximately ten to twenty meters. Combining with map-matching technologies, most navigation systems guide drivers with a best effort manner. In addition to telematics, RFID (Radio Frequency Identification) is an old but newly emerged technology. In this paper, we incorporate RFID technology into a navigation system to improve the accuracy. The skeleton of the idea is as follows: install RFID tags on roads in a certain way, store very accurate location information along with other necessary information in the tags, add an RFID reader module to the navigation system, and use this new location information along with GPS and a gyroscope to produce highly accurate location information. With this scheme, the accuracy of positioning can be dramatically improved, especially in tunnels and in downtown areas. Preliminary results show that this idea is feasible.