scholarly journals Performance Evaluation of GPS Auto-Surveying Techniques

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7374
Author(s):  
João Manito ◽  
José Sanguino
Keyword(s):  
Kalman Filter ◽  
Least Squares ◽  
Unscented Kalman Filter ◽  
Weighted Least Squares ◽  
Base Station ◽  
Global Navigation Satellite Systems ◽  
Precise Position ◽  
Satellite Systems ◽  
Global Navigation Satellite ◽  
Position Estimate

With the increase in the widespread use of Global Navigation Satellite Systems (GNSS), increasing numbers of applications require precise position data. Of all the GNSS positioning methods, the most precise are those that are based in differential systems, such as Differential GNSS (DGNSS) and Real-Time Kinematics (RTK). However, for absolute positioning, the precision of these methods is tied to their reference position estimates. With the goal of quickly auto-surveying the position of a base station receiver, four positioning methods are analyzed and compared, namely Least Squares (LS), Weighted Least Squares (WLS), Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), using only pseudorange measurements, as well as the Hatch Filter and position thresholding. The research results show that the EKF and UKF present much better mean errors than LS and WLS, with an attained precision below 1 m after about 4 h of auto-surveying. The methods that presented the best results are then tested against existing implementations, showing them to be very competitive, especially considering the differences between the used receivers. Finally, these results are used in a DGNSS test, which verifies a significant improvement in the position estimate as the base station position estimate improves.

scholarly journals Kalman Filter-Based RAIM for Reliable Aircraft Positioning with GPS and NavIC Constellations

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6606
Author(s):  
Susmita Bhattacharyya ◽  
Dinesh Mute
Keyword(s):  
Kalman Filter ◽  
Weighted Least Squares ◽  
Unmanned Aircraft ◽  
Global Navigation Satellite Systems ◽  
Navigation Systems ◽  
Satellite Systems ◽  
Vector Tracking ◽  
Receiver Autonomous Integrity Monitoring ◽  
Global Navigation Satellite ◽  
Computational Resources

This paper presents a novel Kalman filter (KF)-based receiver autonomous integrity monitoring (RAIM) algorithm for reliable aircraft positioning with global navigation satellite systems (GNSS). The presented method overcomes major limitations of the authors’ previous work, and uses two GNSS, namely, Navigation with Indian Constellation (NavIC) of India and the Global Positioning System (GPS). The algorithm is developed in the range domain and compared with two existing approaches—one each for the weighted least squares navigation filter and KF. Extensive simulations were carried out for an unmanned aircraft flight path over the Indian sub-continent for validation of the new approach. Although both existing methods outperform the new one, the work is significant for the following reasons. KF is an integral part of advanced navigation systems that can address frequent loss of GNSS signals (e.g., vector tracking and multi-sensor integration). Developing KF RAIM algorithms is essential to ensuring their reliability. KF solution separation (or position domain) RAIM offers good performance at the cost of high computational load. Presented range domain KF RAIM, on the other hand, offers satisfactory performance to a certain extent, eliminating a major issue of growing position error bounds over time. It requires moderate computational resources, and hence, shows promise for real-time implementations in avionics. Simulation results also indicate that addition of NavIC alongside GPS can substantially improve RAIM performance, particularly in poor geometries.

scholarly journals Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8441
Author(s):  
Susmita Bhattacharyya
Keyword(s):  
Kalman Filter ◽  
Measurement Errors ◽  
Weighted Least Squares ◽  
Temporal Variations ◽  
Global Navigation Satellite Systems ◽  
Model Parameters ◽  
Integrity Monitoring ◽  
Satellite Systems ◽  
Performance Analyses ◽  
Global Navigation Satellite

This paper evaluates the performance of an integrity monitoring algorithm of global navigation satellite systems (GNSS) for the Kalman filter (KF), termed KF receiver autonomous integrity monitoring (RAIM). The algorithm checks measurement inconsistencies in the range domain and requires Schmidt KF (SKF) as the navigation processor. First, realistic carrier-smoothed pseudorange measurement error models of GNSS are integrated into KF RAIM, overcoming an important limitation of prior work. More precisely, the error covariance matrix for fault detection is modified to capture the temporal variations of individual errors with different time constants. Uncertainties of the model parameters are also taken into account. Performance of the modified KF RAIM is then analyzed with the simulated signals of the global positioning system and navigation with Indian constellation for different phases of aircraft flight. Weighted least squares (WLS) RAIM used for comparison purposes is shown to have lower protection levels. This work, however, is important because KF-based integrity monitors are required to ensure the reliability of advanced navigation methods, such as multi-sensor integration and vector receivers. A key finding of the performance analyses is as follows. Innovation-based tests with an extended KF navigation processor confuse slow ramp faults with residual measurement errors that the filter estimates, leading to missed detection. RAIM with SKF, on the other hand, can successfully detect such faults. Thus, it offers a promising solution to developing KF integrity monitoring algorithms in the range domain. The modified KF RAIM completes processing in time on a low-end computer. Some salient features are also studied to gain insights into its working principles.

scholarly journals On the Recursive Joint Position and Attitude Determination in Multi-Antenna GNSS Platforms

2020 ◽  
Vol 12 (12) ◽  
pp. 1955 ◽  
Author(s):  
Daniel Medina ◽  
Jordi Vilà-Valls ◽  
Anja Hesselbarth ◽  
Ralf Ziebold ◽  
Jesús García
Keyword(s):  
Attitude Determination ◽  
Base Station ◽  
Joint Estimation ◽  
Global Navigation Satellite Systems ◽  
Transport Systems ◽  
Satellite Systems ◽  
Dynamic Scenario ◽  
Correlated Information ◽  
New Formulation ◽  
Global Navigation Satellite

Global Navigation Satellite Systems’ (GNSS) carrier phase observations are fundamental in the provision of precise navigation for modern applications in intelligent transport systems. Differential precise positioning requires the use of a base station nearby the vehicle location, while attitude determination requires the vehicle to be equipped with a setup of multiple GNSS antennas. In the GNSS context, positioning and attitude determination have been traditionally tackled in a separate manner, thus losing valuable correlated information, and for the latter only in batch form. The main goal of this contribution is to shed some light on the recursive joint estimation of position and attitude in multi-antenna GNSS platforms. We propose a new formulation for the joint positioning and attitude (JPA) determination using quaternion rotations. A Bayesian recursive formulation for JPA is proposed, for which we derive a Kalman filter-like solution. To support the discussion and assess the performance of the new JPA, the proposed methodology is compared to standard approaches with actual data collected from a dynamic scenario under the influence of severe multipath effects.

scholarly journals UWB Base Station Cluster Localization for Unmanned Ground Vehicle Guidance

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Yuzhan Wu ◽  
Susheng Ding ◽  
Yuanhao Ding ◽  
Meng Li
Keyword(s):  
Base Station ◽  
Global Navigation Satellite Systems ◽  
Base Stations ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles ◽  
Satellite Systems ◽  
Area Of Interest ◽  
Narrow Road ◽  
Global Navigation Satellite ◽  
Station Cluster

In this paper, we seek to provide unmanned ground vehicles with positioning service using ultrawideband (UWB) technology, a high-accuracy positioning approach. UWB is chosen for two distinct reasons. First, it does not rely on global navigation satellite systems like GPS, making it able to be applied indoors or in an environment where GPS signal is unstable. Second, it is immune to interference from other signals and accurate enough to guide unmanned ground vehicles moving precisely in a complex environment within a narrow road. In this paper, three UWB base stations are aggregated as a group in a 2D space for localization. A large number of tests are performed with a UWB base station cluster in order to validate its positioning performance. Based on the experiment results, we further develop a dynamic particle swarm optimization-based algorithm and a genetic algorithm to deploy multiple clusters of UWB base stations to cover an area of interest. The performance of the proposed algorithms has been tested through a series of simulations. Finally, experiments using unmanned ground vehicles are carried out to validate the localization performance. The results confirm that the robots can follow complex paths accurately with the proposed UWB-based positioning system.

scholarly journals GNSS-and-GIS based android integration of mobile based virtual guide application ExpLahore for walled city Lahore, Pakistan

2021 ◽  
Vol 2 (1) ◽  
pp. 12-25
Author(s):  
Aaysha Rahim ◽  
Najam Abbas Naqvi
Keyword(s):  
Spatial Information ◽  
Global Navigation Satellite Systems ◽  
Android Application ◽  
Security Risks ◽  
Precise Location ◽  
Precise Position ◽  
Satellite Systems ◽  
Adobe Illustrator ◽  
Exact Position ◽  
Global Navigation Satellite

The expansion of technology by utilizing Global Navigation Satellite Systems (GNSS) integrated with Geographic Information System (GIS) has made life much easier and handy. Global interactions have given birth to tourism and intra-culture programs.  Many foreigners as well as local tourists prefer to explore the sites on their own instead of a guide, this is quite adventurous but can be tedious too and brings along a lot of security risks. This research is based on design and development of an android application considering “Walled City, Lahore, Pakistan”. The application will help tourist geotag their information. This application has been developed with the fact to provide precise position through GNSS, the coordinates have been refined using ArcGIS and QGIS and placed in application using Android Studio and Adobe Illustrator. GIS allowed creating interactive queries, analyzing spatial information and map creation, shape file extraction and 99% precise coordinates than maps we use. In travelling world, geotagging is a great and trending feature that allows user to share their exact position. The application will locate user’s coordinates (Latitude and Longitude), current satellite count, precision, altitude, time and day. The guidance application helps user find the correct possible route to the monuments of walled City, Lahore. Information and precise location of 14 place groups of Lahore city, as well as precise route to them is also a feature of the application. 

The stochastic model for Global Navigation Satellite Systems and terrestrial laser scanning observations: A proposal to account for correlations in least squares adjustment

2019 ◽  
Vol 13 (2) ◽  
pp. 93-104 ◽  
Author(s):  
Gael Kermarrec ◽  
Ingo Neumann ◽  
Hamza Alkhatib ◽  
Steffen Schön
Keyword(s):  
Least Squares ◽  
Laser Scanning ◽  
Global Navigation Satellite Systems ◽  
Deformation Analysis ◽  
Navigation Satellite ◽  
Unknown Parameters ◽  
Satellite Systems ◽  
Unbiased Test ◽  
Global Navigation ◽  
Global Navigation Satellite

Abstract The best unbiased estimates of unknown parameters in linear models have the smallest expected mean-squared errors as long as the residuals are weighted with their true variance–covariance matrix. As this condition is rarely met in real applications, the least-squares (LS) estimator is less trustworthy and the parameter precision is often overoptimistic, particularly when correlations are neglected. A careful description of the physical and mathematical relationships between the observations is, thus, necessary to reach a realistic solution and unbiased test statistics. Global Navigation Satellite Systems and terrestrial laser scanners (TLS) measurements show similarities and can be both processed in LS adjustments, either for positioning or deformation analysis. Thus, a parallel between stochastic models for Global Navigation Satellite Systems observations proposed previously in the case of correlations and functions for TLS range measurements based on intensity values can be drawn. This comparison paves the way for a simplified way to account for correlations for a use in LS adjustment.

scholarly journals GNSS/LiDAR-Based Navigation of an Aerial Robot in Sparse Forests

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4061 ◽  
Author(s):  
Antonio C. B. Chiella ◽  
Henrique N. Machado ◽  
Bruno O. S. Teixeira ◽  
Guilherme A. S. Pereira
Keyword(s):  
Autonomous Navigation ◽  
Unscented Kalman Filter ◽  
Unmanned Vehicles ◽  
Global Navigation Satellite Systems ◽  
Fusion Algorithm ◽  
Forest Environment ◽  
Satellite Systems ◽  
Aerial Robot ◽  
Robust Adaptive ◽  
Global Navigation Satellite

Autonomous navigation of unmanned vehicles in forests is a challenging task. In such environments, due to the canopies of the trees, information from Global Navigation Satellite Systems (GNSS) can be degraded or even unavailable. Also, because of the large number of obstacles, a previous detailed map of the environment is not practical. In this paper, we solve the complete navigation problem of an aerial robot in a sparse forest, where there is enough space for the flight and the GNSS signals can be sporadically detected. For localization, we propose a state estimator that merges information from GNSS, Attitude and Heading Reference Systems (AHRS), and odometry based on Light Detection and Ranging (LiDAR) sensors. In our LiDAR-based odometry solution, the trunks of the trees are used in a feature-based scan matching algorithm to estimate the relative movement of the vehicle. Our method employs a robust adaptive fusion algorithm based on the unscented Kalman filter. For motion control, we adopt a strategy that integrates a vector field, used to impose the main direction of the movement for the robot, with an optimal probabilistic planner, which is responsible for obstacle avoidance. Experiments with a quadrotor equipped with a planar LiDAR in an actual forest environment is used to illustrate the effectiveness of our approach.

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