Stand-alone Global Positioning System Research Articles

TEC and Instrumental Bias Estimation of GAGAN Station Using Kalman Filter and SCORE Algorithm

The standalone Global Positioning System (GPS) does not meet the higher accuracy requirements needed for approach and landing phase of an aircraft. To meet the Category-I Precision Approach (CAT-I PA) requirements of civil aviation, satellite based augmentation system (SBAS) has been planned by various countries including USA, Europe, Japan and India. The Indian SBAS is named as GPS Aided Geo Augmented Navigation (GAGAN). The GAGAN network consists of several dual frequency GPS receivers located at various airports around the Indian subcontinent. The ionospheric delay, which is a function of the total electron content (TEC), is one of the main sources of error affecting GPS/SBAS accuracy. A dual frequency GPS receiver can be used to estimate the TEC. However, line-of-sight TEC derived from dual frequency GPS data is corrupted by the instrumental biases of the GPS receiver and satellites. The estimation of receiver instrumental bias is particularly important for obtaining accurate estimates of ionospheric delay. In this paper, two prominent techniques based on Kalman filter and Self-Calibration Of pseudo Range Error (SCORE) algorithm are used for estimation of instrumental biases. The estimated instrumental bias and TEC results for the GPS Aided Geo Augmented Navigation (GAGAN) station at Hyderabad (78.47°E, 17.45°N), India are presented.

Smart framework for GNSS-based navigation in urban environments

Intelligent Transport System applications require accurate and reliable positioning. When stand-alone global positioning system (GPS) is used in urban areas, the results dramatically degrade, because of signal outages and multipath. This paper unveils new signal processing techniques for carrier phase-based navigation in urban environments. The techniques identify multipath or weak signals using carrier phase based receiver autonomous integrity monitoring, and abnormal signals and situational measurements are eliminated or estimated. To estimate the carrier phase during a short time period when GPS signals is blocked, this paper uses carrier phase statistics. The performance of the proposed method is verified, through a car test. The test environment has many signal outages and multipath because of high buildings. Many abnormal signal conditions occurred during the test, and the results confirmed that the proposed method performed better than the basic stand-alone GPS approach when compared with GPS/inertial navigation system (INS) integrated navigation results. Copyright © 2015 John Wiley & Sons, Ltd.

A Novel Method for Precise Onboard Real-Time Orbit Determination with a Standalone GPS Receiver.

Satellite remote sensing systems require accurate, autonomous and real-time orbit determinations (RTOD) for geo-referencing. Onboard Global Positioning System (GPS) has widely been used to undertake such tasks. In this paper, a novel RTOD method achieving decimeter precision using GPS carrier phases, required by China’s HY2A and ZY3 missions, is presented. A key to the algorithm success is the introduction of a new parameter, termed pseudo-ambiguity. This parameter combines the phase ambiguity, the orbit, and clock offset errors of the GPS broadcast ephemeris together to absorb a large part of the combined error. Based on the analysis of the characteristics of the orbit and clock offset errors, the pseudo-ambiguity can be modeled as a random walk, and estimated in an extended Kalman filter. Experiments of processing real data from HY2A and ZY3, simulating onboard operational scenarios of these two missions, are performed using the developed software SATODS. Results have demonstrated that the position and velocity accuracy (3D RMS) of 0.2–0.4 m and 0.2–0.4 mm/s, respectively, are achieved using dual-frequency carrier phases for HY2A, and slightly worse results for ZY3. These results show it is feasible to obtain orbit accuracy at decimeter level of 3–5 dm for position and 0.3–0.5 mm/s for velocity with this RTOD method.

Retrieving real-time precise co-seismic displacements with a standalone single-frequency GPS receiver

Nowadays, Global Positioning System (GPS) plays an increasingly important role in retrieving real-time precise co-seismic displacements for geo-hazard monitoring and early warning. Several real-time positioning approaches have been demonstrated for such purpose, such as real-time kinematic relative positioning, precise point positioning, etc., where dual-frequency geodetic receivers are applied for the removal of ionosphere delays by inter-frequency combination. At the same time, it would be also useful to develop efficient algorithms for estimating precise displacements with low-cost GPS receivers since they can make a denser network or multi-sensors combination without putting too much financial burden. In this contribution, we present a new method to retrieve precise co-seismic displacements in real-time using a standalone single-frequency receiver. In the new method, observations prior to an earthquake are utilized to establish a precise ionospheric delay prediction model, so that precise co-seismic displacements can be obtained without any convergence process. Our method was validated with an outdoor experiment as well as by re-processing of 1-Hz GPS data collected by the GEONET network during the 2011 Tohoku Mw 9.0 earthquake. For the latter, RMS against dual-frequency receivers constituted 2cm for horizontal components and 3cm for the vertical component.We specially address the observation biases and their impact on the accuracy of single frequency positioning. Our approach makes real-time GPS displacement monitoring with dense network much more affordable in terms of financial costs.

A novel hybrid approach utilizing principal component regression and random forest regression to bridge the period of GPS outages

Global Positioning System (GPS) and Inertial Navigation System (INS) are two salient technologies delivering a vehicle׳s position, velocity and attitude parameters for navigation. The standalone GPS undergoes signal outages (in forest area) and multipath effects (in urban areas), whereas the standalone INS solution accuracy deteriorates with time. To overcome the limitations of standalone GPS and INS, an integrated INS/GPS system is required for continuous, accurate, and reliable navigation solution. This paper proposes a hybrid method of Principal Component Regression (PCR) and Random Forest Regression (RFR) for INS and GPS data fusion.Traditional filter based fusion techniques like Kalman filter (KF), linearized KF, and particle filter (PF) have deficiencies related to sensor error modeling, immunity to noise, and computational load. Artificial Neural Networks (ANN) are introduced to overcome these limitations, but they show poor generalization capability in the presence of noise and suffer from over fitting. Recently, RFR is proposed as an efficient fusion technique due to its flexibility to handle non-linear input–output relationships and overcome the over fitting problems. The limitation with RFR is that it cannot handle both linear and non-linear input–output relationships effectively Hence, the paper introduces PCR, which can model linear input–output relationships. Thus, a hybrid approach of RFR and PCR provides enhanced modeling of input–output relationships leading to increased prediction accuracy and thereby improved integrated INS/GPS system performance. The results are validated through five simulated GPS outages taken on real field test data. The proposed model showed a maximum of 45.06% decrement in positional error when compared to RFR.

An Integrated Vehicle Navigation System Utilizing Lane-Detection and Lateral Position Estimation Systems in Difficult Environments for GPS

A navigation filter combines measurements from sensors currently available on vehicles - Global Positioning System (GPS), inertial measurement unit, inertial measurement unit (IMU), camera, and light detection and ranging (lidar) - for achieving lane-level positioning in environments where stand-alone GPS can suffer or fail. Measurements from the camera and lidar are used in two lane-detection systems, and the calculated lateral distance (to the lane markings) estimates of both lane-detection systems are compared with centimeter-level truth to show decimeter-level accuracy. The navigation filter uses the lateral distance measurements from the lidar- and camera-based systems with a known waypoint-based map to provide global measurements for use in a GPS/Inertial Navigation System (INS) system. Experimental results show that the inclusion of lateral distance measurements and a height constraint from the map creates a fully observable system even with only two satellite observations and, as such, greatly enhances the robustness of the integrated system over GPS/INS alone. Various scenarios are presented, which affect the navigation filter, including satellite geometry, number of satellites, and loss of lateral distance measurements from the camera and lidar systems.

Accuracy assessment of GPS navigation augmented by SAR and LiDAR-derived Digital Elevation Models

Remotely sensed Digital Elevation Models (DEM) can be used to augment a standalone Global Positioning System (GPS) by adding an extra range observation which measures the distance to the Earth centre. This method so called height aiding can reduce the number of GPS satellites required to get a 3D position fix from four to three and hence improve the performance of the GPS navigation algorithm in terms of accuracy, reliability and availability. Up until now, the accuracy of height aided GPS navigation using higher resolution Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR)-derived elevation data has not been fully evaluated in a broad spectrum of navigation scenarios. This article provides a robust and accurate analysis on how much range error is introduced by height aiding using 5 m spacing SAR and 1 m spacing LiDAR-derived DEMs under in-car and personal navigation situations. Based on the experimental results obtained from both dynamic and static tests, suggestions have been made on what level of vertical and positional accuracy can be achieved as well as the related DEM quality issues for navigation purposes.

Simplistic approach for water vapour sensing using a standalone global positioning system receiver

Precipitable water vapour (PWV) is an important input for numerical weather prediction model, meteorology and high-precision navigational applications. Conventional methods for the determination of PWV using radiosonde are not sufficient owing to poor temporal resolution, whereas radiometer-derived PWV is reliable only in fair weather conditions. Global positioning system (GPS) is a very useful and cost-effective tool to determine PWV continuously in all weather conditions. The processing of GPS data to extract the PWV information is, however, very complicated due to very small effect of the PWV (~0.5% of total delay) on GPS frequencies than other sources of delay and errors and requires a network of GPS in differential configuration for such purpose. The authors show how the problem can be handled in a standalone dual-frequency GPS receiver in a relatively less complicated manner with reasonable accuracy. The performances of different dry tropospheric delay models are also investigated. The methodology is tested with GPS measurements at Kolkata (22.57°N, 88.37°E) and Bangalore (13.01°N, 77.5°E). The results indicate that the proposed methodology can be implemented for PWV estimation using single GPS receiver with satisfactory performance.

Global Navigation Satellite Systems Seismology for the 2012 Mw 6.1 Emilia Earthquake: Exploiting the VADASE Algorithm

Online Material: Tables of statistical indexes; figures comparing displacement solutions. The Global Positioning System (GPS) has been repeatedly proven to be a powerful tool to estimate coseismic displacements and waveforms, with accuracies ranging from few millimeters to few centimeters. These promising results were achieved following two main strategies: differential positioning (DP) and precise point positioning (PPP; Bock et al. [1993], Kouba [2003], Larson et al. [2007], Larson [2009], Ohta et al. [2012], Xu et al. [2012], and Hung and Rau [2013]). In particular, both the modeling of fault rupture and the seismic moment estimation could benefit from GPS‐derived displacements, because GPS is not affected by the saturation problems experienced by seismometers located near the epicenters of strong earthquakes. Thanks to the robustness of the GPS‐derived displacement waveforms, in the last years some authors (Bock et al. , 2000; Langbein and Bock, 2004; Blewitt et al. , 2006; Bock and Genrich, 2006) addressed the problem to retrieve them in real time, with accuracies of a few centimeters, from GPS high‐rate observations (1 Hz or more). In this context, the Variometric Approach for Displacements Analysis Standalone Engine (VADASE) has been proposed (Colosimo et al. [2011a], Colosimo [2013]). The approach is based on time single differences of carrier phase observations continuously collected using a standalone GPS receiver and on standard GPS broadcast products (orbits and clocks) that are available in real time. Therefore, one receiver works in standalone mode and the epoch‐by‐epoch displacements (equivalent to velocities) are estimated. Then, they are summed over the time interval when the earthquake occurred to retrieve displacements. Because VADASE does not require either additional technological complexity or a centralized data analysis, in principle, it can be embedded into the GPS receiver firmware and therefore can work in real time. Moreover, differently from DP and PPP, …

Portable device to assess dynamic accuracy of global positioning system (GPS) receivers used in agricultural aircraft.

A device was designed to test the dynamic accuracy of Global Positioning System (GPS) receivers used in agricultural aircraft and other aerial vehicles. The system works by directing a sun-reflected light beam from the ground to the aircraft using mirrors. A photo detector points downward from the aircraft to detect the light beam, and photo detection circuitry triggers an event in the guidance system data file at the aircraft’s location corresponding to the precisely georeferenced position on the ground. Construction details are presented on the mirror-based light reflection system and photo-electronic circuitry designed to trigger an event in the guidance system’s log file. An example application evaluated the horizontal accuracy of a stand-alone GPS receiver by matching dynamic data with data from the aircraft’s guidance system. Results indicated a 2.16 s lead in position registered by the stand-alone receiver over that registered by the aircraft’s guidance system GPS receiver, which had been previously evaluated to be within 0.13 s of Real-Time Kinematic (RTK)-referenced time and position. Keywords: GPS receiver accuracy, agricultural aircraft, aerial application, remote sensing, georeferencing, UAV DOI: 10.3965/j.ijabe.20140702.008 Citation: Thomson S J, Huang Y B, Smith L A. Portable device to assess dynamic accuracy of global positioning system (GPS) receivers used in agricultural aircraft. Int J Agric & Biol Eng, 2014; 7(2): 68－74.

Observability Analysis of Collaborative Opportunistic Navigation With Pseudorange Measurements

The observability analysis of a collaborative opportunistic navigation (COpNav) environment whose states may be partially known is considered. A COpNav environment can be thought of as a radio frequency (RF) signal landscape within which one or more RF receivers locate themselves in space and time by extracting and, possibly, sharing information from ambient signals of opportunity (SOPs). These receivers, whether vehicle mounted or integrated into handheld devices, exploit signal diversity to improve navigation and timing robustness compared with stand-alone Global Positioning System (GPS) receivers in deep urban, indoor, or, otherwise, GPS-hostile environments. Available SOPs may have a fully known, partially known, or unknown characterization. In this paper, the receivers are assumed to draw only pseudorange-type measurements from the SOPs. Separate observations are fused to produce an estimate of each receiver's position, velocity, and time (PVT). Since not all SOP states in the COpNav environment may be known a priori, the receivers must estimate the unknown SOP states of interest simultaneously with their own PVT. This paper establishes the minimal conditions under which a COpNav environment consisting of multiple receivers and multiple SOPs is completely observable. Moreover, in scenarios where the COpNav environment is unobservable, the unobservable directions in the state space are specified. Simulation and experimental results are presented to confirm the theoretical observability conditions.

Study and analysis of Differential GNSS and Precise Point Positioning

The Global Positioning system (GPS) is used for navigation and associated tasks in wide variety of applications. One of the primary modes of GPS operation is differential GPS. Differential GPS allows more accurate positioning than stand-alone GPS. Many new applications are possible because of the increased accuracy obtained through differential GPS. In this paper the techniques and methods of differential GPS along with the algorithms are discussed which are providing more accurate and precise positioning in recent trends of communication. The purpose of the paper is to introduce the principles of differential GPS theory, and to provide a background for more advanced material.

Integration of GPS with a WiFi high accuracy ranging functionality

High accuracy seamless positioning is required to support a vast number of applications in varying operational environments. Over the last few years, the global positioning system (GPS) has become the de facto technology for positioning applications. However, its performance is limited in indoor and dense urban environments due to multipath as well as signal attenuation and blockage. A number of techniques integrating GPS with other positioning technologies have been developed to address the limitations of standalone GPS in these difficult environments. While most of the developed techniques cover the outages of GPS in such environments, they do not provide acceptable performance, in terms of positioning accuracy, especially for some mission-critical (e.g. safety) applications. This paper proposes a tightly coupled (i.e. in the measurement domain) GPS/WiFi integration method which, in addition to addressing GPS outages, improves the overall positioning accuracy to the meter-level, thus satisfying the requirements of a number of location based services and intelligent transport systems applications. The performance of the proposed GPS/WiFi integration method is assessed for a number of scenarios in a simulation environment for an identified dense urban area in London, UK.

Seamless INS/GPS Integration Based on Support Vector Machines

The combination of Inertial Navigation System (INS) and Global Positioning System (GPS) provides superior performance in comparison with either a stand-alone INS or GPS. However, the positioning accuracy of INS/GPS deteriorates with time in the absence of GPS signals. A least squares support vector machines (LS-SVM) regression algorithm is applied to INS/GPS integrated navigation system to bridge the GPS outages to achieve seamless navigation. In this method, LS-SVM is trained to model the errors of INS when GPS is available. Once the LS-SVM is properly trained in the training phase, its prediction can be used to correct the INS errors during GPS outages. Simulations in INS/GPS integrated navigation showed improvements in positioning accuracy when GPS outages occur.

Neural-network multiple models filter (NMM)-based position estimation system for autonomous vehicles

A highly accurate and reliable vehicle position estimation system is an important component of an autonomous driving system. In generally, a global positioning system (GPS) receiver is employed for the vehicle position estimation of autonomous vehicles. However, a stand-alone GPS does not always provide accurate and reliable information of the vehicle position due to frequent GPS blockages and multipath errors. In order to overcome these problems, a sensor fusion scheme that combines the data from the GPS receiver and several on-board sensors has been studied. In previous researches, a single model filter-based sensor fusion algorithm was used to integrate information from the GPS and on-board sensors. However, an estimate obtained from a single model is difficult to cover the various driving environments, including urban areas, off-road areas, and highways. Thus, a multiple models filter (MMF) has been introduced to address this limitation by adapting multiple models to a wide range of driving conditions. An adaptation of the multiple model is achieved through the use of the model probability. The MMF combines several vehicle models using the model probabilities, which indicate the suitability of the current driving condition. In this paper, we propose a vehicle position estimation algorithm for an autonomous vehicle that is based on a neural network (NN)-based MMF. The model probabilities are determined through the NN. The proposed position estimation system was evaluated through simulations and experiments. The experimental results show that the proposed position estimation algorithm is suitable for application in an autonomous driving system over a wide range of driving conditions.

Road Grade Measurement Using In-Vehicle, Stand-Alone GPS with Barometric Altimeter

Real-world vehicle fuel use and emission rates are sensitive to road grade. There is a need for a practical method for measuring road grade in combination with on-board measurement of vehicle activity, energy use, and emissions using portable emissions measurement systems (PEMS). This paper focuses on quantification of the accuracy and precision of a low-cost method using a stand-alone global positioning system (GPS) receiver with an in-built barometric altimeter. Approximately 100 one-way runs were made on each of several study routes. The sensitivity of average grade estimates to the averaging distance over which grade is estimated is quantified. The repeatability of vehicle location and distance traveled is quantified. The run-to-run variability and confidence intervals for average estimates of grade are quantified. The accuracy of the grade estimates is evaluated in comparison to LIDAR-based estimates. The low-cost method is shown to be accurate, but imprecision in the measurements leads to a need for typically at least 10 or more repeated runs, depending on the desired precision of the average estimate of grade.

Millimeter-accuracy GPS landslide monitoring using Precise Point Positioning with Single Receiver Phase Ambiguity (PPP-SRPA) resolution: a case study in Puerto Rico

Continuous Global Positioning System (GPS) monitoring is essential for establishing the rate and pattern of superficial movements of landslides. This study demonstrates a technique which uses a stand-alone GPS station to conduct millimeter-accuracy landslide monitoring. The Precise Point Positioning with Single Receiver Phase Ambiguity (PPP-SRPA) resolution employed by the GIPSY/OASIS software package (V6.1.2) was applied in this study. Two-years of continuous GPS data collected at a creeping landslide were used to evaluate the accuracy of the PPP-SRPA solutions. The criterion for accuracy was the root-mean-square (RMS) of residuals of the PPP-SRPA solutions with respect to “true” landslide displacements over the two-year period. RMS is often regarded as repeatability or precision in GPS literature. However, when contrasted with a known ”true” position or displacement it could be termed RMS accuracy or simply accuracy. This study indicated that the PPP-SRPA resolution can provide an accuracy of 2 to 3 mm horizontally and 8 mm vertically for 24-hour sessions with few outliers (< 1%) in the Puerto Rico region. Horizontal accuracy below 5 mm can be stably achieved with 4-hour or longer sessions if avoiding the collection of data during extreme weather conditions. Vertical accuracy below 10 mm can be achieved with 8-hour or longer sessions. This study indicates that the PPP-SRPA resolution is competitive with the conventional carrier-phase double-difference network resolution for static (longer than 4 hours) landslide monitoring while maintaining many advantages. It is evident that the PPP-SRPA method would become an attractive alternative to the conventional carrier-phase double-difference method for landslide monitoring, notably in remote areas or developing countries.

GPS Output Signal Processing considering both Correlated/White Measurement Noise for Optimal Navigation Filtering

In this paper, a dynamic modeling for the velocity and position information of a single frequency stand-alone GPS(Global Positioning System) receiver is described. In static condition, the position error dynamic model is identified as a first/second order transfer function, and the velocity error model is identified as a band-limited Gaussian white noise via non-parametric method of a PSD(Power Spectrum Density) estimation in continuous time domain. A Kalman filter is proposed considering both correlated/white measurements noise based on identified GPS error model. The performance of the proposed Kalman filtering method is verified via numerical simulation.

ENHANCING KALMAN FILTERING–BASED TIGHTLY COUPLED NAVIGATION SOLUTION THROUGH REMEDIAL ESTIMATES FOR PSEUDORANGE MEASUREMENTS USING PARALLEL CASCADE IDENTIFICATION

Integrated global positioning system (GPS) solutions that utilize micro-electro-mechanical systems (MEMS)-based inertial sensors provide a more accurate navigation solution than stand-alone GPS in challenging scenarios. To keep the integrated solution less affected by sensor errors and to decrease the cost, a reduced inertial sensor system (RISS), which consists of only one gyroscope and two accelerometers, together with an odometer and integrated with GPS, is proposed. Tightly coupled integration is a better choice in demanding scenarios, as it can provide GPS aiding even when the number of visible satellites is three or less. However, inaccuracies of pseudoranges measured by the GPS receiver and used as aiding in the RISS/odometer/GPS integration solution will affect the overall positioning accuracy. This article explores the benefits of using parallel cascade identification (PCI), a nonlinear system identification technique that improves the overall navigation solution by modeling residual pseudorange correlated errors to be used by a Kalman filter (KF)–based tightly coupled RISS/odometer/GPS navigational solution. When less than four satellites are visible, the identified parallel cascade model for the still visible satellites is used to predict the residual pseudorange errors for these respective satellites, and the corrected pseudorange value is provided to KF. The performance of PCI for correcting the pseudoranges is examined and verified using road test trajectories and compared to a traditional tightly coupled RISS/odometer/GPS KF solution. The results demonstrate the advantages of this technique in correcting the pseudoranges and enhancing the positional solution.

A new source difference artificial neural network for enhanced positioning accuracy

Integrated inertial navigation system (INS) and global positioning system (GPS) units provide reliable navigation solution compared to standalone INS or GPS. Traditional Kalman filter-based INS/GPS integration schemes have several inadequacies related to sensor error model and immunity to noise. Alternatively, multi-layer perceptron (MLP) neural networks with three layers have been implemented to improve the position accuracy of the integrated system. However, MLP neural networks show poor accuracy for low-cost INS because of the large inherent sensor errors. For the first time the paper demonstrates the use of knowledge-based source difference artificial neural network (SDANN) to improve navigation performance of low-cost sensor, with or without external aiding sources. Unlike the conventional MLP or artificial neural networks (ANN), the structure of SDANN consists of two MLP neural networks called the coarse model and the difference model. The coarse model learns the input–output data relationship whereas the difference model adds knowledge to the system and fine-tunes the coarse model output by learning the associated training or estimation error. Our proposed SDANN model illustrated a significant improvement in navigation accuracy of up to 81% over conventional MLP. The results demonstrate that the proposed SDANN method is effective for GPS/INS integration schemes using low-cost inertial sensors, with and without GPS.