Ahmed El-Rabbany

Tightly coupled integration of GPS precise point positioning and MEMS-based inertial systems

Abstract We develop a new integrated navigation system, which integrates GPS precise point positioning (PPP) with low-cost micro-electro-mechanical sensors (MEMS) inertial system, for precise positioning applications. Currently, most common GPS PPP techniques employ undifferenced ionosphere-free (IF) linear combination. In this work, both undifferenced and between-satellite single-difference (BSSD) IF linear combinations of pseudorange and carrier measurements are considered. IGS precise orbital and clock products are used to correct for satellite orbit and clock errors. Rigorous models are used to account for tropospheric delay, ocean loading, earth tide, carrier phase windup, relativity and satellite antenna phase-center variations. To integrate GPS PPP and MEMS-based inertial systems, the process and measurement models are developed. Tightly coupled mechanization is adopted, which is carried out in the raw measurements domain. Extended Kalman filter is developed to merge the corrected GPS satellite difference observations and inertial measurements and estimate inertial measurements biases and errors. A Matlab-based computer program is developed to carry out the tightly coupled integration. The performance of the proposed integrated system is analyzed using a real test situation. It is shown that decimeter-level positioning accuracy is achievable with both undifferenced and BSSD integrated systems. However, in general, better positioning accuracy is obtained with BSSD integrated system.

An Efficient Neural Network Model for De-noising of MEMS-Based Inertial Data

Micro-Electro-Mechanical System (MEMS)-based inertial technology has recently evolved. It holds remarkable potential as the future technology for various navigation related applications. This is mainly due to the significant reduction in size, cost, and weight of MEMS sensors. A major drawback of low-cost MEMS-based inertial sensors, however, is that their output signals are contaminated by high-level noise. Unless the high frequency noise component is suppressed, optimizing the pre-filtering methodology cannot be achieved. This paper proposes a neural network-based de-noising model for MEMS-based inertial data. A modular, three-layer feedforward neural network trained using the back-propagation algorithm is used for this purpose. Simulated and real MEMS-based inertial data sets are used to validate the model. It is shown that the model is capable of reducing the noise of the Crossbows AHRS300CA IMU data by over one order of magnitude without altering the stochastic nature of the original signal. This is of utmost importance in developing a generic stochastic model for MEMS-based inertial data. A comparison between the developed neural network model and the wavelet de-noising method is made to further validate the model. It is shown that achieving the same level of noise suppression with wavelet-based de-noising model changes the stochastic characteristics of original signal.

Mobile vision-based vehicle tracking and traffic control

This paper discusses work-in-progress to develop a mobile, bus-mounted machine vision system for transit and traffic monitoring in urban corridors, as required by Intelligent Transportation Systems. In contrast to earlier machine vision technologies used for traffic management, which mainly rely on simple algorithms to detect certain traffic characteristics, the new proposed approach makes use of a recent trend in computer vision research: namely the active vision paradigm. Active vision systems have mechanisms that can actively control camera parameters, such as orientation, focus, zoom, and convergence, in response to the requirements of the task and external stimuli. Mounting active vision systems on buses will have the advantage of providing real-time feedback of the current traffic conditions while possessing the intelligence and visual skills which allow them to interact with a rapidly changing dynamic environment such as moving traffic.

Integration of GPS precise point positioning and MEMS-based INS using unscented particle filter.

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) integrated system involves nonlinear motion state and measurement models. However, the extended Kalman filter (EKF) is commonly used as the estimation filter, which might lead to solution divergence. This is usually encountered during GPS outages, when low-cost micro-electro-mechanical sensors (MEMS) inertial sensors are used. To enhance the navigation system performance, alternatives to the standard EKF should be considered. Particle filtering (PF) is commonly considered as a nonlinear estimation technique to accommodate severe MEMS inertial sensor biases and noise behavior. However, the computation burden of PF limits its use. In this study, an improved version of PF, the unscented particle filter (UPF), is utilized, which combines the unscented Kalman filter (UKF) and PF for the integration of GPS precise point positioning and MEMS-based inertial systems. The proposed filter is examined and compared with traditional estimation filters, namely EKF, UKF and PF. Tightly coupled mechanization is adopted, which is developed in the raw GPS and INS measurement domain. Un-differenced ionosphere-free linear combinations of pseudorange and carrier-phase measurements are used for PPP. The performance of the UPF is analyzed using a real test scenario in downtown Kingston, Ontario. It is shown that the use of UPF reduces the number of samples needed to produce an accurate solution, in comparison with the traditional PF, which in turn reduces the processing time. In addition, UPF enhances the positioning accuracy by up to 15% during GPS outages, in comparison with EKF. However, all filters produce comparable results when the GPS measurement updates are available.

Performance analysis of NOAA tropospheric signal delay model

Tropospheric delay is one of the dominant global positioning system (GPS) errors, which degrades the positioning accuracy. Recent development in tropospheric modeling relies on implementation of more accurate numerical weather prediction (NWP) models. In North America one of the NWP-based tropospheric correction models is the NOAA Tropospheric Signal Delay Model (NOAATrop), which was developed by the US National Oceanic and Atmospheric Administration (NOAA). Because of its potential to improve the GPS positioning accuracy, the NOAATrop model became the focus of many researchers. In this paper, we analyzed the performance of the NOAATrop model and examined its effect on ionosphere-free-based precise point positioning (PPP) solution. We generated 3 year long tropospheric zenith total delay (ZTD) data series for the NOAATrop model, Hopfield model, and the International GNSS Services (IGS) final tropospheric correction product, respectively. These data sets were generated at ten IGS reference stations spanning Canada and the United States. We analyzed the NOAATrop ZTD data series and compared them with those of the Hopfield model. The IGS final tropospheric product was used as a reference. The analysis shows that the performance of the NOAATrop model is a function of both season (time of the year) and geographical location. However, its performance was superior to the Hopfield model in all cases. We further investigated the effect of implementing the NOAATrop model on the ionosphere-free-based PPP solution convergence and accuracy. It is shown that the use of the NOAATrop model improved the PPP solution convergence by 1%, 10% and 15% for the latitude, longitude and height components, respectively.

Assessment of Several Interpolation Methods for Precise GPS Orbit

GPS applications such as Precise Point Positioning (PPP) require the availability of precise ephemeris at high rate. To support these applications, several institutions such as the International GNSS Service (IGS) have developed precise orbital service. Unfortunately, however, the data rate of such precise orbits is usually limited to 15 minutes. To overcome this problem, a number of orbital interpolation methods are proposed. This paper examines the performance of four interpolation methods for IGS precise GPS orbits, namely Lagrange, Newton Divided Difference, Cubic Spline and Trigonometric interpolation. In addition, the paper discusses a new approach, which utilizes the residuals between the broadcast and precise ephemeris to generate a high density precise ephemeris. It is shown that the new approach produces better results than previously reported orbital interpolation accuracy.

Single Frequency Multipath Mitigation Based On Wavelet Analysis

Multipath is still one of the major error sources that degrades the accuracy of GPS positioning. The amount of multipath is highly dependent on the antennas environment, which makes it difficult to isolate. Usually there is at least one in-view satellite which is more susceptible to multipath, particularly the one with the lowest elevation angle. To increase the positioning the best satellites must be selected (i.e. by least square or multipath mitigation) for computing a position. In this paper we propose an algorithm which picks up the best satellites (when there are more than four satellites in view) based on wavelet analysis for calculating a position. In this experiment, code and carrier measurements were collected in 15-minute segments by exploiting a single frequency (L1), stationary, navigation-grade receiver in a high-multipath environment. The magnitudes of these pseudoranges were often inflated by multipath error. We then post-processed the received data by applying wavelet filtering to the residuals (code minus carrier) to approximate the multipath values, and compute the receivers position based on the selected satellites. Satellites were selected based on the residual values. To compare the results with the raw measurements, statistical elements were computed. The results showed significant improvement in variance of the estimated positions and, most importantly, a normalization of the data scatter-distribution was observed.

Precise Point Positioning using Multi-Constellation GNSS Observations for Kinematic Applications

Abstract Traditional precise point positioning (PPP) is commonly based on un-differenced ionosphere-free linear combination of Global Positioning System (GPS) observations. Unfortunately, for kinematic applications, GPS often experiences poor satellite visibility or weak satellite geometry in urban areas. To overcome this limitation, we developed a PPP model, which combines the observations of three global navigation satellite systems (GNSS), namely GPS, GLONASS and Galileo. Both un-differenced and between-satellite single-difference (BSSD) ionosphere-free linear combinations of pseudorange and carrier phase GNSS measurements are processed. The performance of the combined GNSS PPP solution is compared with the GPS-only PPP solution using a real test scenario in downtown Kingston, Ontario. Inter-system biases between GPS and the other two systems are also studied and obtained as a byproduct of the PPP solution. It is shown that the addition of GLONASS observations improves the kinematic PPP solution accuracy in comparison with that of GPS-only solution. However, the contribution of adding Galileo observations is not significant due to the limited number of Galileo satellites launched up to date. In addition, BSSD solution is found to be superior to that of traditional un-differenced model.

Efficient Between-Satellite Single-Difference Precise Point Positioning Model

AbstractThis paper describes a new model for precise point positioning (PPP), which overcomes the drawbacks of existing PPP models. This model is based on between-satellite single-difference (BSSD), which cancels out the receiver clock error, receiver initial phase bias, and receiver hardware delay. The decoupled clock (DC) corrections, which were provided by Natural Resources Canada (NRCan), were applied to account for code and carrier-phase satellite clock corrections, satellite hardware delay, and satellite initial phase bias. The results from three PPP models (namely, undifferenced-DC, BSSD, and BSSD-DC) were compared with the traditional (i.e., undifferenced) PPP solution. It is shown that the proposed BSSD models improves the PPP convergence time by up to 50% and improves the solution precision by more than 60% in comparison with the traditional PPP model.

On stochastic modeling of the modernized global positioning system (GPS) L2C signal

In order to take full advantage of the modernized GPS L2C signal, it is essential that its stochastic characteristics and code bias be rigorously determined. In this paper, long sessions of GPS measurements are used to study the stochastic characteristics of the modernized GPS L2C signal. As a byproduct, the stochastic characteristics of the legacy GPS signals, namely C/A and P2 codes, are also determined, which are used to verify the developed stochastic model of the modernized signal. The differential code biases between P2 and C2, DCBP2-C2, are also estimated using the Bernese GPS software. It is shown that the developed models improved the precise point positioning (PPP) solution and convergence time.