Rapid Transfer Alignment of SINS with Measurement Packet Dropping based on a Novel Suboptimal Estimator

Transfer alignment (TA) is an important step for strapdown inertial navigation system (SINS) starting from a moving base, which utilises the information proposed from the higher accurate and well performed master inertial navigation system. But the information is often delayed or even lost in real application, which will seriously affect the accuracy of TA. This paper models the stochastic measurement packet dropping as an independent identically distributed (IID) Bernoulli random process, and introduces it into the measurement equation of rapid TA, and the influence of measurement packet dropping is analysed. Then, it presents a suboptimal estimator for the estimation of the misalignment in TA considering the random arrival of the measurement packet. Simulation has been done for the performance comparison about the suboptimal estimator, standard Kalman filter and minimum mean squared estimator. The results show that the suboptimal estimator has better performance, which can achieve the best TA accuracy

SIRU development. Volume 1: System development

A complete description of the development and initial evaluation of the Strapdown Inertial Reference Unit (SIRU) system is reported. System development documents the system mechanization with the analytic formulation for fault detection and isolation processing structure; the hardware redundancy design and the individual modularity features; the computational structure and facilities; and the initial subsystem evaluation results

Navigation System Design with Application to the Ares I Crew Launch Vehicle and Space Launch Systems

For a launch vehicle, the Navigation System is responsible for determining the vehicle state and providing state and state derived information for Guidance and Controls. The accuracy required of the Navigation System by the vehicle is dependent upon the vehicle, vehicle mission, and other consideration, such as impact foot print. NASAs Ares I launch vehicle and SLS are examples of launch vehicles with are/where to employ inertial navigation systems. For an inertial navigation system, the navigation system accuracy is defined by the inertial instrument errors to a degree determined by the method of estimating the initial navigation state. Utilization of GPS aiding greatly reduces the accuracy required in inertial hardware to meet the same accuracy at orbit insertion. For a launch vehicle with lunar bound payload, the navigation accuracy can have large implications on propellant required to correct for state errors during trans-lunar injection

Anti-disturbance fault tolerant initial alignment for inertial navigation system subjected to multiple disturbances

Modeling error, stochastic error of inertial sensor, measurement noise and environmental disturbance affect the accuracy of an inertial navigation system (INS). In addition, some unpredictable factors, such as system fault, directly affect the reliability of INSs. This paper proposes a new anti-disturbance fault tolerant alignment approach for a class of INSs sub- jected to multiple disturbances and system faults. Based on modeling and error analysis, stochastic error of inertial sensor, measurement noise, modeling error and environmental disturbance are formulated into different types of disturbances described by a Markov stochastic process, Gaussian noise and a norm-bounded variable, respectively. In order to improve the accuracy and reliability of an INS, an anti-disturbance fault tolerant filter is designed. Then, a mixed dissipative/guarantee cost performance is applied to attenuate the norm-bounded disturbance and to optimize the estimation error. Slack variables and dissipativeness are introduced to reduce the conservatism of the proposed approach. Finally, compared with the unscented Kalman filter (UKF), simulation results for self-alignment of an INS are provided based on experimental data. It can be shown that the proposed method has an enhanced disturbance rejection and attenuation performance with high reliability

Enhancing 3D Autonomous Navigation Through Obstacle Fields: Homogeneous Localisation and Mapping, with Obstacle-Aware Trajectory Optimisation

Small flying robots have numerous potential applications, from quadrotors for search and rescue, infrastructure inspection and package delivery to free-flying satellites for assistance activities inside a space station. To enable these applications, a key challenge is autonomous navigation in 3D, near obstacles on a power, mass and computation constrained platform. This challenge requires a robot to perform localisation, mapping, dynamics-aware trajectory planning and control. The current state-of-the-art uses separate algorithms for each component. Here, the aim is for a more homogeneous approach in the search for improved efficiencies and capabilities. First, an algorithm is described to perform Simultaneous Localisation And Mapping (SLAM) with physical, 3D map representation that can also be used to represent obstacles for trajectory planning: Non-Uniform Rational B-Spline (NURBS) surfaces. Termed NURBSLAM, this algorithm is shown to combine the typically separate tasks of localisation and obstacle mapping. Second, a trajectory optimisation algorithm is presented that produces dynamically-optimal trajectories with direct consideration of obstacles, providing a middle ground between path planners and trajectory smoothers. Called the Admissible Subspace TRajectory Optimiser (ASTRO), the algorithm can produce trajectories that are easier to track than the state-of-the-art for flight near obstacles, as shown in flight tests with quadrotors. For quadrotors to track trajectories, a critical component is the differential flatness transformation that links position and attitude controllers. Existing singularities in this transformation are analysed, solutions are proposed and are then demonstrated in flight tests. Finally, a combined system of NURBSLAM and ASTRO are brought together and tested against the state-of-the-art in a novel simulation environment to prove the concept that a single 3D representation can be used for localisation, mapping, and planning

The 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies

This publication comprises the papers presented at the 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland, on May 9-11, 1995. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed

1999 Flight Mechanics Symposium

This conference publication includes papers and abstracts presented at the Flight Mechanics Symposium held on May 18-20, 1999. Sponsored by the Guidance, Navigation and Control Center of Goddard Space Flight Center, this symposium featured technical papers on a wide range of issues related to orbit-attitude prediction, determination, and control; attitude sensor calibration; attitude determination error analysis; attitude dynamics; and orbit decay and maneuver strategy. Government, industry, and the academic community participated in the preparation and presentation of these papers

Application of aircraft's flight testing techniques to the aerodynamic characterization of power kites

This thesis has developed an experimental methodology for the flight testing and data analysis of power kites applied to Airborne Wind Energy Systems (AWES). In particular, the Estimation Before Modeling technique, a well-known method in the aerospace industry for the aerodynamic characterization of an aircraft using real flight data, has been adapted for tethered aircraft. The developed methodology has two main building blocks: (i) an experimental setup to record experimental data during the flight testing, and (ii) a Flight Path Reconstruction algorithm to estimate the state of the system from the experimental data. From them, the aerodynamic characteristics of two types of kites were investigated. The proposed experimental setup was designed to be low cost, portable and easily adaptable to both, rigid and semi-rigid kites. It is composed of an instrumented kite representative of the ones used in AWES, an instrumented control bar, a ground computer and a wind station. Whenever it was possible, commercial off the shelf components have been used, including low cost openhardware sensors based on the PixHawk platform. However, after the first flight tests were conducted and the obtained results were discussed, high precision sensors were also included. The Flight Path Reconstruction (FPR) algorithm for tethered aircraft is based on an Extended Kalman Filter (EKF). In addition to the standard set of estimated state variables (ie. Euler angles, position or ground speed), the algorithm also provides the aerodynamic torque and forces upon the kite as well as the tether tensions and wind velocity vector. The EBM technique, and the FPR algorithm have been used to identify the aerodynamic characteristics of both, four-line Leading Edge Inflatable (LEI) kites and two-line Rigid Frame Delta (RFD) kites. Quantitative and qualitative results have been obtained. Albeit both types of kites exhibited very high AoA during the flight, some significant differences were found. In particular, the estimated lift coefficient of the LEI kite showed a behavior identified with a post-stall condition, while the RFD showed a pre-stall behavior with a lower AoA and a positive relation between the lift coefficient and the kite AoA. The presented experimental methodology can be of great interest for AWE industry as it helps to improve modeling of tethered aircraft, leading to more accurate performance figures which may increase investors interest in the technology. Moreover, flight testing methodologies and experimental data analysis are of great interest for benchmarking AWES performances, contributing to de-risk their development process and providing better tools for AWE "best concept" identification. Finally, as a sub-product of the presented methodology, the FPR algorithm can be used as a validated state estimator of the tethered aircraft, which is a key element of a closed loop flight control system.Programa de Doctorado en Mecánica de Fluidos por la Universidad Carlos III de Madrid; la Universidad de Jaén; la Universidad de Zaragoza; la Universidad Nacional de Educación a Distancia; la Universidad Politécnica de Madrid y la Universidad Rovira i VirgiliPresidente: Marco Fontana.- Secretario: Manuel García-Villalba Navaridas.- Vocal: Félix Terroba Ramíre