An Artificial Neural Network Embedded Position and Orientation Determination Algorithm for Low Cost MEMS INS/GPS Integrated Sensors

Digital mobile mapping, which integrates digital imaging with direct geo-referencing, has developed rapidly over the past fifteen years. Direct geo-referencing is the determination of the time-variable position and orientation parameters for a mobile digital imager. The most common technologies used for this purpose today are satellite positioning using Global Positioning System (GPS) and Inertial Navigation System (INS) using an Inertial Measurement Unit (IMU). They are usually integrated in such a way that the GPS receiver is the main position sensor, while the IMU is the main orientation sensor. The Kalman Filter (KF) is considered as the optimal estimation tool for real-time INS/GPS integrated kinematic position and orientation determination. An intelligent hybrid scheme consisting of an Artificial Neural Network (ANN) and KF has been proposed to overcome the limitations of KF and to improve the performance of the INS/GPS integrated system in previous studies. However, the accuracy requirements of general mobile mapping applications can’t be achieved easily, even by the use of the ANN-KF scheme. Therefore, this study proposes an intelligent position and orientation determination scheme that embeds ANN with conventional Rauch-Tung-Striebel (RTS) smoother to improve the overall accuracy of a MEMS INS/GPS integrated system in post-mission mode. By combining the Micro Electro Mechanical Systems (MEMS) INS/GPS integrated system and the intelligent ANN-RTS smoother scheme proposed in this study, a cheaper but still reasonably accurate position and orientation determination scheme can be anticipated

Navigation algorithm for INS/GPS Data Fusion

Diplomová práce se zabývá návrhem algoritmu rozšířeného Kalmanova filtru, který integruje data z inerciálního navigačního systému (INS) a globálního polohovacího systému (GPS). Součástí algoritmu je i samotná mechanizace INS, určující na základě dat z akcelerometrů a gyroskopů údaje o rychlosti, zeměpisné pozici a polohových úhlech letadla. Vzhledem k rychlému nárůstu chybovosti INS je výstup korigován hodnotami rychlosti a pozice získané z GPS. Výsledný algoritmus je implementován v prostředí Simulink. Součástí práce je odvození jednotlivých stavových matic rozšířeného Kalmanova filtru.This diploma thesis deals with Extended Kalman Filter algorithm fusing data from inertial navigation system (INS) and Global Positioning System (GPS). The part of the developed algorithm is a mechanization of INS which processes data from accelerometers and gyroscopes to provide velocity, position and attitude angles. Due to rapid increase of INS output errors, the EKF is used to correct INS outputs by velocity and position from GPS. The final algorithm is developed in Simulink environment. This thesis includes derivation of EKF state matrices.

UKF Based filters in INS/GPS integrated navigation systems: Novel application results

The filtering problem in the INS/GPS integrated navigation system is investigated in this study. Firstly, the nonlinear model of the INS/GPS integrated navigation system is proposed. Then the applications of the EKF, the UKF and the modified adaptive UKF are put in practice based on the model. A sample set of computer simulation results for the three filter algorithms, obtained by using MATLAB platform, are given to illustrate the effectiveness of the proposed algorithm with regard to the convergence rate and the estimation precision. Copyright © 2007 IFAC Keywords: Adaptive signal processing, integrated navigation system, INS/GPS, trace tracking, unscented Kalman filter, nonlinear control system

Airborne Wind Profiling Algorithm for Doppler Wind LIDAR

Systems, methods, and devices of the present invention enable airborne Doppler Wind LIDAR system measurements and INS/GPS measurements to be combined to estimate wind parameters and compensate for instrument misalignment. In a further embodiment, the wind speed and wind direction may be computed based on two orthogonal line-of-sight LIDAR returns

A Micromechanical INS/GPS System for Small Satellites

The cost and complexity of large satellite space missions continue to escalate. To reduce costs, more attention is being directed toward small lightweight satellites where future demand is expected to grow dramatically. Specifically, micromechanical inertial systems and microstrip global positioning system (GPS) antennas incorporating flip-chip bonding, application specific integrated circuits (ASIC) and MCM technologies will be required. Traditional microsatellite pointing systems do not employ active control. Many systems allow the satellite to point coarsely using gravity gradient, then attempt to maintain the image on the focal plane with fast-steering mirrors. Draper's approach is to actively control the line of sight pointing by utilizing on-board attitude determination with micromechanical inertial sensors and reaction wheel control actuators. Draper has developed commercial and tactical-grade micromechanical inertial sensors, The small size, low weight, and low cost of these gyroscopes and accelerometers enable systems previously impractical because of size and cost. Evolving micromechanical inertial sensors can be applied to closed-loop, active control of small satellites for micro-radian precision-pointing missions. An inertial reference feedback control loop can be used to determine attitude and line of sight jitter to provide error information to the controller for correction. At low frequencies, the error signal is provided by GPS. At higher frequencies, feedback is provided by the micromechanical gyros. This blending of sensors provides wide-band sensing from dc to operational frequencies. First order simulation has shown that the performance of existing micromechanical gyros, with integrated GPS, is feasible for a pointing mission of 10 micro-radians of jitter stability and approximately 1 milli-radian absolute error, for a satellite with 1 meter antenna separation. Improved performance micromechanical sensors currently under development will be suitable for a range of micro-nano-satellite applications

Indirect Estimation of Vertical Ground Reaction Force from a Body-Mounted INS/GPS Using Machine Learning

Vertical ground reaction force(vGRF)can be measured by forceplates or instrumented treadmills, but their application is limited to indoor environments. Insoles remove this restriction but suffer from low durability (several hundred hours). Therefore, interest in the indirect estimation of vGRF using inertial measurement units and machine learning techniques has increased. This paper presents a methodology for indirectly estimating vGRF and other features used in gait analysis from measurements of a wearable GPS-aided inertial navigation system (INS/GPS) device. A set of 27 features was extracted from the INS/GPS data. Feature analysis showed that six of these features suffice to provide precise estimates of 11 different gait parameters. Bagged ensembles of regression trees were then trained and used for predicting gait parameters for a dataset from the test subject from whom the training data were collected and for a dataset from a subject for whom no training data were available. The prediction accuracies for the latter were significantly worse than for the first subject but still sufficiently good. K-nearest neighbor (KNN) and long short-term memory (LSTM) neural networks were then used for predicting vGRF and ground contact times. The KNN yielded a lower normalized root mean square error than the neural network for vGRF predictions but cannot detect new patterns in force curves.publishedVersionPeer reviewe

Coupling architecture between INS/GPS for precise navigation on set paths

GPS offers the advantage of providing high long-term position accuracy with residual errors that affect the final positioning solution to a few meters with a sampling frequency of 1 Hz (Marston et al. in Decis Support Syst 51:176–189, 2011 [1]). The signals are also subject to obstruction and interference, so GPS receivers cannot be relied upon for a continuous navigation solution. On the contrary, the inertial navigation system has a sampling frequency of at least 50 Hz and exhibits low noise in the short term. In this research, a prototype based on development cards is implemented for the coupling of the inertial navigation system with GPS to improve the precision of navigation on a trajectory

INS/GPS Based State Estimation of Micro Air Vehicles Using Inertial Sensors

The most attractive and attention seeking topic of aerospace world is Micro Air Vehicle (MAV) which broadly speaking it can be categorized as significantly smaller aircraft than conventional aircrafts. Micro Air Vehicles can be divided as autonomous, semi-autonomous and remote controlled flying machines which can be fixed wing MAV, Flapping wing MAV and rotary wing MAV. One of the crucial problems regarding Micro Air Vehicle is its stability which is basically concerned with the guidance, navigation and control. State Estimation is an important aspect of navigation and this paper deals with the state estimation problem of micro Air vehicle. Inertial sensors are being used including MEMS Gyro, MEMS Accelerometer, Magnetometer and GPS in Inertial Measurement Unit (IMU) and three Discrete Time Extended Kalman Filter Schemes have been used for the state estimation purpose. Trajectory and required data is recorded in Flight Simulator and MATLAB has been used for the simulations. A comprehensive parametric study is carried out and results are analyzed and briefly discussed. Keywords: Micro Electro Mechanical System (MEMS), Micro Air Vehicle (MAV), Measurement Covariance Matrix, Process Covariance Matri