Design, Develop and Test Software to Study the Rate of Climb for GPS Paraglide

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) are very practical method to get high precision navigation. GPS/INS data Fusion in navigation has been a research topic for decades. Usually the update time of GPS an INS is different. The update time of GPS is 20HZ, and the time of INS is 100HZ or higher. Since GPS and INS are mainly methods in navigation system. Techniques of fusing GPS and INS data are in high demand in navigation industry. Usually the Karman filter is used to fuse the data from GPS and INS. But the Kalman filter is model-dependent and a priori the model parameters need to be known. In this work, a generic and intelligent approach of GPS and INS data fuse using fuzzy interpolation before Kalman algorithm is proposed. First a simplified interpolation model is introduced in a ordinary GPS and INS data fusion. Second a fuzzy rule-based system is established to fuzzy interpolation with objective fuzzy method. Then the parameters of objective fuzzy system are identified, the best n-rule is determinate and the optimal rule base system is found. Finally this approach is applied on an example for a car GPS and INS data fusion, and the results are calculated with computer system to demonstrate the advantages of this approach.

Global Positioning System and Inertial Navigation System can be used to determine position and velocity. A Global Positioning System module is able to accurately determine position without sensor drift, but its usage is limited in heavily urbanized environments and heavy vegetation. While high-cost tactical-grade Inertial Navigation System can determine position accurately, low-cost micro-electro-mechanical system Inertial Navigation System sensors are plagued by significant errors. Global Positioning System is coupled with Inertial Navigation System to correct the errors, while Inertial Navigation System itself can be used to provide navigation solution during a Global Positioning System outage. Data from Global Positioning System and Inertial Navigation System can be integrated by extensive Kalman filtering, using loosely coupled integration architecture to provide navigation solutions. In this study, real-time low-cost loosely coupled micro-electro-mechanical system Inertial Navigation System/Global Positioning System sensors have been used for pedestrian navigation. Trial runs of Global Positioning System outages have been conducted to determine the accuracy of the system described. The micro-electro-mechanical system Inertial Navigation System/Global Positioning System can successfully project a trajectory during a Global Positioning System outage and produces a root mean square error of 9.35 m in latitude direction and 10.8 m in longitude direction. This technology is very suitable for visually impaired pedestrians.

In present days, real time navigation depends on Kalman filter to fuse data from Global Positioning System (GPS) and Inertial Navigation System (INS). But there exist drawbacks like long design time, requirement of prior knowledge in fusing data from GPS and INS using Kalman Filter. In order to overcome the drawbacks of Kalman filter, GPS and INS integration are done using Artificial Neural Networks (ANN) like FFNNs. But still there exists certain drawbacks like less accuracy in FFNNs for GPS and INS data integration. Therefore, this paper introduces Higher Order Neural Networks (HONNs) approach for INS and GPS data integration to overcome the drawback. A higher order feed forward neural network architecture with optimum number of nodes is used. In the given architectures, the replacement of summation at each node by multiplication results in more powerful mapping, because of its capability of processing higher-order information from training data. Performance comparison of HONNs with FFNNs shows that the proposed architecture provides satisfactory results in terms of error rate and number of epochs. The HONNs like Multiplicative Neural Network (MNN) module and Sigma-Pi-ANN module and Traditional Feed Forward Neural Networks(FFNNs) like Radial Basis Function Neural Network (RBF) module and Back propagation Neural Network (BPN) module are trained to predict the INS position error and provide accurate position of the moving aircrafts. Sigma-Pi ANN is found to be the best in terms of accuracy, number of epochs and execution time among the two HONNs.

The aim of the paper is to investigate, design and construct the Global Positioning System (GPS) navigation system, communications bus, and central computer for an autonomous ocean vessel. The objectives were: to interface a GPS receiver to a host processor; to design a communications bus connecting the central computer to the host processor; and to design the central computer. The software to control the GPS receiver remotely from the central computer has been completed. The host processor is now able to request the GPS receiver for any message, and is able to command the receiver to perform different tasks and change current settings. Buffers have been incorporated into the code to increase efficiency. The host processor can now transmit to and receive from the central computer across the communications network. A marine navigational bus standard, NMEA 0183, has been researched and found suitable for this application. Interfaces to the bus have been constructed and tested. The functionality of the central computer has been implemented on an IBM-PC compatible, and is able to control the host processor. This paper aims to inform of the results obtained in the above mentioned areas.

How to achieve continuous, reliable and accurate positioning performance using low-cost sensors is one of the main challenges for aviation navigation system. Global Positioning System (GPS) can provide the primary means of navigation in a number of aviation navigation applications (e.g., manned and unmanned aircraft vehicle, airport ground vehicle). However, GPS signal deteriorations typically occur due to aircraft itself during maneuvering, ionospheric scintillation, Doppler shift, multipath and so on. Thus, there is a need to research GPS augmentation strategies which can be used in the Communication, Navigation, Surveillance/Air Traffic Management. GPS integration with Inertial Navigation system (INS) is one of the key strategies. But once GPS signal outages, the integrated navigation system works in pure INS, and positioning accuracy deteriorates with time. When using low cost GPS/INS integration, a primary problem is the rapid performance deteriorate during GPS outages. To provide continuous, accurate and reliable positioning information in aviation, discrete grey prediction model (DGPM) aided fusion methodology is proposed. The DGPM provides pseudo-GPS position information for INS during GPS outages. The mathematical model of integrated navigation system is established, including INS error equations, Kalman filter and DGPM. The model works in the update mode when there is no GPS failure, whereas it switches to the prediction mode in case of GPS outages. To verify the feasibility and effectiveness of the proposed methodology, real road test is performed. The comparison results show that accuracy of longitude and latitude are improved by more than 80% and 70%, respectively. The DGPM can effectively provide position corrections for standalone INS during GPS outages.

With the development of the vehicle industry, controlling stability has become more and more important. Techniques of evaluating vehicle stability are in high demand. Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) is a very practical method to get high-precision measurement data. Usually, the Kalman filter is used to fuse the data from GPS and INS. In this paper, a robust method is used to measure vehicle sideslip angle and yaw rate, which are two important parameters for vehicle stability. First, a four-wheel vehicle dynamic model is introduced, based on sideslip angle and yaw rate. Second, a double level Kalman filter is established to fuse the data from Global Positioning System and Inertial Navigation System. Then, this method is simulated on a sample vehicle, using Carsim software to test the sideslip angle and yaw rate. Finally, a real experiment is made to verify the advantage of this approach. The experimental results showed the merits of this method of measurement and estimation, and the approach can meet the design requirements of the vehicle stability controller.

Neural networks have been proposed as nonlinear filters in a variety of applications that involve nonlinear processing of input signals; examples include blind signal separation, image registration, and blind deconvolution. The Global Positioning System (GPS) navigation equations are nonlinear (quadratic) in nature, and a direct closed form solution of the GPS navigation equations does not exist. This article presents a new approach to solving the GPS pseudorange equations using three-layer neural networks. A three-layer radial basis function (RBF) neural network is designed, which solves the non-linear GPS pseudorange equations directly as opposed to the linear least squares or extended Kalman filter approaches in traditional GPS receivers. For training the neural network, a carefully selected cost function is minimized using a variation of the classical conjugate gradient algorithm such that training time for the neural network is reasonable. Simulations have been performed at SiRF Technology Inc. that show stable behavior even under bad geometry conditions where the traditional recursive least squares and extended Kalman filter approaches show high sensitivity to measurement errors. Under good geometry conditions the neural network solution shows slightly improved noise performance compared to the expected performance of traditional leas squares solution. Simulations have been performed with additive white Gaussian noise and correlated noise models to evaluate the performance of the trained neural network. © 2000 John Wiley & Sons, Inc.

A smart city’s main purpose is to provide intelligent responses to different problems of the rapid urban population growth. For instance, integrating fleet management solutions into intelligent transportation systems (ITS) can efficiently resolve transportation problems relying on each vehicle information. Usually, the position estimate is ensured by the integration of the Global Positioning System (GPS) and Inertial Navigation Systems (INS). For multisensor data fusion, the Extended Kalman Filter (EKF) is generally applied using the sensor’s measures and the GPS position as a helper. However, the INS are expensive and require more complex computing which induces restrictions on their implementation. Furthermore, the EKF performance depends on the vehicle dynamic variations and may quickly diverge because of environmental changes. In this paper, we present a robust low cost approach using EKF and Support Vector Machines (SVM) to reliably estimate the vehicle position by limiting the EKF drawbacks. The sensors used are a GPS augmented by a low cost wireless sensor network. When GPS signals are available, we train SVM on different dynamics and outage times to learn the position errors so we can correct the future EKF predictions during GPS signal outages. We obtain empirically an improvement of up to 94% over the simple EKF predictions in case of GPS failures.

This paper describes radio interference analysis between Global Positioning System (GPS) and Industrial Scientific Medical (ISM) equipment. Due to increase of the usage of ISM equipment, the potential possibility of interference from ISM equipment to GPS system which used for safety life has been increasing. For the interference analysis between ISM equipment and GPS system, the scenario that GPS receiver operates in close to ISM equipment was set up. Then, protection distance between GPS receiver and ISM equipment was calculated in order to protect GPS system. The analysis result will be helpful for providing stable operation of GPS system.

In the integration of Global Positioning System (GPS) and Inertial Navigation System (INS), the commonly used Kalman filter provides satisfactory results if both sources of information are continuously available. However, GPS outages provoke a fast degradation of precision, especially in low dynamic trajectories such as a mobile platform device held by a human operator. To deal with this problem we propose a data-filtering scheme to apply to INS raw data prior to the integration with GPS. The proposed technique proves to be very valuable for mitigating the high short-term instability of raw INS data during the walking movement and is also capable of eliminating the induced undesirable human operator vibrations. Final imposed corrections adapted to the particular dynamical response of the INS sensor provide comparably accurate results and often better than those achieved in similar works with the use of the Kalman filter.

In modern navigation applications, Inertial Navigation System (INS) is attractive for integrating with Global Positioning System (GPS). Although traditional GPS/INS integrated systems can bridge the GPS gaps, the navigation capability is strongly dependent on the performances of standalone INS. As an important complementary, a new terrestrial, Radio-Frequency (RF) based, distance measurement technology “Locata” can provide continuously time-synchronised ranging signals even in many GPS challenged environments e.g. poor satellite geometry, signal blockage in suburban, tunnels, high rise buildings canyon. This paper investigates the integration of Locata, GPS, and INS with a focus on the loosely-coupled triple integration algorithm. Firstly, the Conventional Kalman Filtering (CKF) based triple integration of Locata/GPS/INS architecture is described and briefly discussed. Secondly, to overcome the pitfalls of conventional Locata/GPS/INS integration algorithm, an Adaptive Dual Kalman Filtering (ADKF) algorithm is proposed and developed in three stages: (1) To enhance the reliability of position and velocity (PV) quantities generated from Locata/GPS integrated sensors, the 1st KF is additionally constructed to reliably estimate the PV solution before fusing INS sensor. (2) Combining the 15-state INS error model and the measurements which are the differences between PV solution from the 1st KF and INS sensor, the 2nd KF is subsequently employed to correct the INS navigation errors. (3) The final integration solution is reversely used as the feedback for precisely estimating the stochastic model (i.e. variance of dynamic and observation model noise) of the 1st KF. Finally, the real flight experiment is carried out to demonstrate the efficiency and validity of Locata/GPS/INS integration algorithms. The results show that: (1) Conventional GPS/INS integration performs well but its accuracy dramatically decreases when GPS signals are unavailable for a short period. (2) Augmented by Locata, GPS/INS produces tolerable results in whole experiment even without aiding of GPS. (3) By additionally operation of the 1st KF and adaptively estimating its stochastic model with feedback of integration solution, ADKF achieves more accurate and reliable position, velocity and attitude (PVA) solution than CKF.KeywordsINS errorLocata/GPS/INS integrationAdaptive Kalman filteringStochastic model

This paper describes the theoretical and practical stages through development to testing of an integrated navigation system, specifically composed of an Inertial Navigation System (INS), and Global Positioning System (GPS). Among various INS/GPS integration strategies, our aim is to construct a mathematical model of INS and GPS systems. A Kalman filter is designed and implemented depending upon these models. Besides these, the performance of the developed system is evaluated with real data recorded by the sensors. A comparison with a reference system and also with a loosely coupled system is done to show the Superiority of the tightly coupled structure. The strap down inertial navigation system (SINS) uses (ENU) frame and the integrated system uses position and velocity as measurements. The system model of the integrated system for Kalman filtering are derived and modeled as 27-states. These states estimation system showed clearly the application of fundamental modeling and filtering techniques. The simulation is built on the integrated system INS/GPS/GNSS and the trajectory generator data for flight simulation to be used as a base line algorithm contributing for design, analysis and develop a guidance and control strategy which compensates for the effects of the uncertainties accompanying the navigation systems produced by INS alignment.

With the development of the intelligent vehicle navigation systems and vehicle networking systems, vehicle positioning and state estimation become very important. This paper investigates the integration of Global Positioning System (GPS), Inertial Navigation System (INS) and Dead Reckoning based on Anti-lock Braking System (ABSDR) positioning based on Kalman filter, and aims at achieving full-time vehicle positioning. In this paper, we use Kalman Filter to estimate INS positioning error based on GPS or ABSDR solution, take the INS solution compensated with estimated error as system output. This paper introduces INS positioning algorithm, dead reckoning method based on the ABS wheel speed and Kalman filtering method. After that, simulation analysis and experimental verification of the positioning algorithm are carried out. The results show that the system output high-precision high-frequency positioning information when GPS is available, and when GPS is unavailable, INS positioning errors can be compensated well with ABSDR introduced, achieving the full-time positioning of the vehicle.

Global Positioning System (GPS) and Inertial Navigation System (INS) data can be integrated to provide a reliable navigation. This paper presents an approach of solving GPS/INS data integration problem, without the need of modeling the characteristics of GPS and INS sensors. Use of Artificial Intelligence (AI) techniques for an intelligent navigation system has been developed as an alternative to the conventional Kalman filter approach, in which it is mandatory to model the entire system. Many AI techniques have been implemented for the same, in which the use of ANFIS instead of neural networks and fuzzy logic has been widely implemented. In this paper Memetic optimization on ANFIS (MANFIS) has been proposed which outperforms Genetically optimized ANFIS (GANFIS).

This paper demonstrates the use of Global Positioning System (GPS) and Inertial Navigation System (INS) in order to develop an Unmanned Ground Vehicle (UGV) devised to perform a wide variety of outdoor tasks. There are many applications for autonomous UGVs such as tactical and surveillance applications, exploration of areas inaccessible by humans. Capable to navigate to a specific location, and control their motion depending on their surroundings without human intervention. The inertial navigation system makes use of Inertial Measurement Units (IMUs) to measure the change to the UGV's positional parameters, orientation and speed which are continuously monitored and updated. With the advent of GPS, and the positional data from the inertial system the positional information is computed leading to a more accurate control of the UGV; which otherwise suffers from integration drift that occurs with the implementation of inertial systems alone. Autonomous control of the UGV is implemented by coupling GPS sensor and Mission Planner, a tool to map waypoints from Google Maps. Furthermore, system stability and ideal PID (Proportional, Integral and Derivative) values are determined using bicycle modeling analysis to achieve better estimates and control of the UGV. DOI: http://dx.doi.org/10.11591/ijece.v4i3.5183