Optimising the Integration of Terrain Referenced Navigation with INS and GPS

2005 ◽  
Vol 59 (1) ◽  
pp. 71-89 ◽  
Author(s):  
Paul D Groves ◽  
Robin J Handley ◽  
Andrew R Runnalls
Keyword(s):  
Gaussian Mixture ◽  
Gps Data ◽  
Low Grade ◽  
Radar Altimeter ◽  
Processing Efficiency ◽  
Navigation Solution ◽  
Gps Navigation ◽  
Terrain Referenced Navigation ◽  
Simulation Results ◽  
Navigation Accuracy

The benefits of integrated INS/GPS systems are well known. However, the knowledge required to jam GPS is becoming public and the hardware to achieve this is basic. When GPS data are unavailable and a low grade INS is used, navigation accuracy quickly degrades to an unacceptable level. The addition of one or more terrain referenced navigation (TRN) systems to an integrated INS/GPS navigation system enables the INS to be calibrated during GPS outages, increasing the robustness of the overall navigation solution. TRN techniques are compared and integration architectures are reviewed. For the initial studies of INS/GPS/TRN integration, radar altimeter based terrain contour navigation (TCN) with a batch processing algorithm is used in conjunction with a centralised integration filter. Four different approaches for using these TCN fixes to calibrate the INS are compared. These are a best fix method, a weighted fix method using a probabilistic data association filter (PDAF) and single and multi-hypothesis versions of the Iterative Gaussian Mixture Approximation of the Posterior (IGMAP) method. Simulation results are presented showing that the single hypothesis IGMAP technique offers the best balance between accuracy, robustness and processing efficiency.

scholarly journals Ant-Mutated Immune Particle Filter Design for Terrain Referenced Navigation with Interferometric Radar Altimeter

2021 ◽  
Vol 13 (11) ◽  
pp. 2189
Author(s):  
Suktae Kang ◽  
Myeong-Jong Yu
Keyword(s):  
Immune System ◽  
Particle Filter ◽  
Measurement Errors ◽  
Filter Design ◽  
Sar Interferometry ◽  
Low Grade ◽  
Radar Altimeter ◽  
Terrain Referenced Navigation ◽  
Auxiliary Particle ◽  
Auxiliary Particle Filter

This study aims to design a robust particle filter using artificial intelligence algorithms to enhance estimation performance using a low-grade interferometric radar altimeter (IRA). Based on the synthetic aperture radar (SAR) interferometry technology, the IRA can extract three-dimensional ground coordinates with at least two antennas. However, some IRA uncertainties caused by geometric factors and IRA-inherent measurement errors have proven to be difficult to eliminate by signal processing. These uncertainties contaminate IRA outputs, crucially impacting the navigation performance of low-grade IRA sensors in particular. To deal with such uncertainties, an ant-mutated immune particle filter (AMIPF) is proposed. The proposed filter combines the ant colony optimization (ACO) algorithm with the immune auxiliary particle filter (IAPF) to bring individual mutation intensity. The immune system indicates the stochastic parameters of the ACO, which conducts the mutation process in one step for the purpose of computational efficiency. The ant mutation then moves particles into the most desirable position using parameters from the immune system to obtain optimal particle diversity. To verify the performance of the proposed filter, a terrain referenced navigation (TRN) simulation was conducted on an unmanned aerial vehicle (UAV). The Monte Carlo simulation results show that the proposed filter is not only more computationally efficient than the IAPF but also outperforms both the IAPF and the auxiliary particle filter (APF) in navigation performance and robustness.

Mitigation of Multipath Errors Using the Iterative Least Absolute Deviation Approach

2015 ◽  
Vol 764-765 ◽  
pp. 555-559
Author(s):  
Dah Jing Jwo ◽  
Meng Hsien Hsieh
Keyword(s):  
Multipath Propagation ◽  
Least Absolute Deviation ◽  
Absolute Deviation ◽  
Navigation Solution ◽  
Gps Navigation ◽  
Robust Estimates ◽  
Statistical Inferences ◽  
Alternative Approach ◽  
Simulation Results ◽  
Multipath Errors

The least squares (LS) approach has been widely used for solving the GPS navigation solution. Despite its many superior properties, however, the LS estimate can be sensitive to outliers and its performance in terms of accuracy and statistical inferences may be compromised when the errors are large and heterogeneous. The GPS signal is strongly affected by the multipath propagation errors. The LS is not able to cope with the above condition to provide a useful and plausible solution. In this paper, an alternative approach based on the least absolute deviation (LAD) criterion for estimating the navigation solution is carried out. The LAD method, which is also known as the L1 method, provides a useful and plausible solution. Unlike the LS method, the LAD method is not as sensitive to the outliers and so as to provide more robust estimates. Simulation results show that the method can effectively mitigate the GPS multipath errors.

scholarly journals Numerical modelling of agglomeration process in a granular bed containing melting particles

2021 ◽  
Vol 2057 (1) ◽  
pp. 012063
Author(s):  
I G Donskoy
Keyword(s):  
Thermal Decomposition ◽  
Porous Medium ◽  
Numerical Model ◽  
Numerical Modelling ◽  
Side Wall ◽  
Low Grade ◽  
Granular Bed ◽  
Agglomeration Process ◽  
Simulation Results ◽  
Implicit Numerical Method

Abstract The paper considers a numerical model of a flow in a porous medium containing particles of a melting component (polymer). For this, an implicit numerical method of splitting in directions is used. Calculations are carried out for two heating methods (through the side wall, or by the input gas). The simulation results qualitatively reproduce some of the experimentally observed features of the thermal decomposition of polymer-containing mixtures. The results obtained are of interest in the study of low-grade fuels processing, often accompanied by agglomeration, as well as in the development of methods by which agglomeration can be prevented.

scholarly journals Data Driven Robust Energy and Reserve Dispatch Based on a Nonparametric Dirichlet Process Gaussian Mixture Model

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4642
Author(s):  
Li Dai ◽  
Dahai You ◽  
Xianggen Yin
Keyword(s):  
Robust Optimization ◽  
Dirichlet Process ◽  
Forecast Error ◽  
Optimization Methods ◽  
Optimization Method ◽  
Gaussian Mixture ◽  
Data Driven ◽  
Uncertainty Sets ◽  
Uncertainty Set ◽  
Simulation Results

Traditional robust optimization methods use box uncertainty sets or gamma uncertainty sets to describe wind power uncertainty. However, these uncertainty sets fail to utilize wind forecast error probability information and assume that the wind forecast error is symmetrical and independent. This assumption is not reasonable and makes the optimization results conservative. To avoid such conservative results from traditional robust optimization methods, in this paper a novel data driven optimization method based on the nonparametric Dirichlet process Gaussian mixture model (DPGMM) was proposed to solve energy and reserve dispatch problems. First, we combined the DPGMM and variation inference algorithm to extract the GMM parameter information embedded within historical data. Based on the parameter information, a data driven polyhedral uncertainty set was proposed. After constructing the uncertainty set, we solved the robust energy and reserve problem. Finally, a column and constraint generation method was employed to solve the proposed data driven optimization method. We used real historical wind power forecast error data to test the performance of the proposed uncertainty set. The simulation results indicated that the proposed uncertainty set had a smaller volume than other data driven uncertainty sets with the same predefined coverage rate. Furthermore, the simulation was carried on PJM 5-bus and IEEE-118 bus systems to test the data driven optimization method. The simulation results demonstrated that the proposed optimization method was less conservative than traditional data driven robust optimization methods and distributionally robust optimization methods.

scholarly journals A model of the novel concept liquid piston engine sourced by waste heat

2018 ◽  
Vol 240 ◽  
pp. 05003
Author(s):  
Wojciech Bujalski ◽  
Kamil Futyma ◽  
Jarosław Milewski ◽  
Arkadiusz Szczęśniak
Keyword(s):  
Waste Heat ◽  
Low Grade ◽  
The Novel ◽  
Piston Engine ◽  
Aspen Hysys ◽  
Proposed Model ◽  
Power Plant Operation ◽  
Simulation Results ◽  
Novel Concept ◽  
Liquid Piston

This paper describes the model of the novel concept liquid piston engine, which is designed to convert low-grade waste heat into electricity. The proposed dynamic oriented model is implemented in Aspen Hysys that enables simulations dynamic simulation of various working agents. The simulation results were verified with experimental data obtained from the research installation. The proposed model demonstrated relatively small discrepancies with respect to experimental research, hence it could be used as a tool for research on optimization of an innovative power plant operation, i.e. various working agents, various operating pressures.

scholarly journals Results of the GNSS receiver experiment OCAM-G on Ariane-5 flight VA 219

2016 ◽  
Vol 231 (6) ◽  
pp. 1100-1114 ◽  
Author(s):  
André Hauschild ◽  
Markus Markgraf ◽  
Oliver Montenbruck ◽  
Horst Pfeuffer ◽  
Elie Dawidowicz ◽  
...  
Keyword(s):  
French Guiana ◽  
Satellite System ◽  
Navigation Satellite ◽  
Navigation Solution ◽  
Gnss Receiver ◽  
Upper Stage ◽  
Lift Off ◽  
Global Navigation Satellite ◽  
Navigation Accuracy ◽  
First Time

The fifth Automated Transfer Vehicle was launched on 29 July 2014 with Ariane-5 flight VA 219 into orbit from Kourou, French Guiana. For the first time, the ascent of an Ariane rocket was independently tracked with a Global Navigation Satellite System (GNSS) receiver on this flight. The GNSS receiver experiment OCAM-G was mounted on the upper stage of the rocket. Its receivers tracked the trajectory of the Ariane-5 from lift-off until after the separation of the Automated Transfer Vehicle. This article introduces the design of the experiment and presents an analysis of the data gathered during the flight with respect to the GNSS tracking status, availability of navigation solution, and navigation accuracy.

scholarly journals Algorithms of Position and Velocity Estimation in GPS Receivers

2016 ◽  
Vol 23 (1) ◽  
pp. 53-68 ◽  
Author(s):  
Piotr Kaniewski ◽  
Rafał Gil ◽  
Stanisław Konatowski
Keyword(s):  
Global Positioning System ◽  
Velocity Estimation ◽  
Frame Of Reference ◽  
Positioning System ◽  
Gps Receivers ◽  
Extended Kalman Filters ◽  
Navigation Solution ◽  
Gps Satellites ◽  
Global Positioning ◽  
Simulation Results

Abstract Processing of signals in Global Positioning System (GPS) receivers includes numerous signal and data operations leading to calculation of coordinates and velocities of satellites in global Earth-Centered Earth-Fixed (ECEF) frame of reference as well as pseudoranges and delta-ranges between the user and all the tracked GPS satellites. Further processing of these data consists in estimation of the user’s position, velocity and time (PVT) and nowadays it is usually realized by means of an Extended Kalman Filters (EKF). The choice of measuring data processed by the Kalman filter significantly influences the accuracy of navigation solution. In simpler GPS receivers, the estimation of user’s position and velocity is based on pseudoranges only, whereas in more advanced ones delta-ranges are also applied. The paper describes both possible solutions and compares the accuracy of estimation of the user’s position and velocity in both cases. The comparison is based on simulation results, which are included in the paper.

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