Model predictive control system design and implementation for spacecraft rendezvous

This paper presents the design and implementation of a model predictive control (MPC) system to guide and control a chasing spacecraft during rendezvous with a passive target spacecraft in an elliptical or circular orbit, from the point of target detection all the way to capture. To achieve an efficient system design, the rendezvous manoeuvre has been partitioned into three main phases based on the range of operation, plus a collision-avoidance manoeuvre to be used in event of a fault. Each has its own associated MPC controller. Linear time-varying models are used to enable trajectory predictions in elliptical orbits, whilst a variable prediction horizon is used to achieve finite-time completion of manoeuvres, and a 1-norm cost on velocity change minimises propellant consumption. Constraints are imposed to ensure that trajectories do not collide with the target. A key feature of the design is the implementation of non-convex constraints as switched convex constraints, enabling the use of convex linear and quadratic programming. The system is implemented using commercial-off-the-shelf tools with deployment using automatic code generation in mind, and validated by closed-loop simulation. A significant reduction in total propellant consumption in comparison with a baseline benchmark solution is observed

Lunar navigation study, sections 1 through 7 Final report, Jun. 1964 - May 1965

Lunar navigation analysis using passive nongyro, inertial navigation, and radio frequency technolog

Navigation using Vector and Tensor Measurements of the Earth\u27s Magnetic Anomaly Field

This research explores the viability of using a navigation system that relies on measurements of the magnetic anomaly field as an alternative to GPS navigation. Previous research has been conducted on developing a navigation system using the intensity of the Earth\u27s magnetic anomaly field as an alternative signal. This research focuses on using vector and tensor measurements, as opposed to scalar measurements of the anomaly field, as a means of obtaining accurate position and orientation solutions. This paper presents two navigation systems. The first uses an Extended Kalman Filter (EKF) with vector measurements of the magnetic anomaly field to aid an inertial navigation system (INS), while the second uses tensor measurements. Simulations examine the performance of both navigation systems in sixteen scenarios. The parameters evaluated in the simulations include the position and velocity of the trajectory, whether vector or tensor measurements are used, the quality of the INS paired with the filter, and the map resolution. Simulations demonstrate that the tensor measurement filter paired with a navigation-grade INS performed best out of the sixteen test cases. For a one-hour ship trajectory, the navigation system was able to demonstrate 35.94 m DRMS error when paired with a navigation-grade INS. The same navigation system was able to obtain navigation accuracies of 38.10 m DRMS when paired with a 10X-grade INS for a 25 hour ship trajectory with a lower resolution magnetic field map due to the depth of the ocean

An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data.

Road surface monitoring and maintenance are essential for driving comfort, transport safety and preserving infrastructure integrity. Traditional road condition monitoring is regularly conducted by specially designed instrumented vehicles, which requires time and money and is only able to cover a limited proportion of the road network. In light of the ubiquitous use of smartphones, this paper proposes an automatic pothole detection system utilizing the built-in vibration sensors and global positioning system receivers in smartphones. We collected road condition data in a city using dedicated vehicles and smartphones with a purpose-built mobile application designed for this study. A series of processing methods were applied to the collected data, and features from different frequency domains were extracted, along with various machine-learning classifiers. The results indicated that features from the time and frequency domains outperformed other features for identifying potholes. Among the classifiers tested, the Random Forest method exhibited the best classification performance for potholes, with a precision of 88.5% and recall of 75%. Finally, we validated the proposed method using datasets generated from different road types and examined its universality and robustness

Three-D multilateration: A precision geodetic measurement system

A technique of satellite geodesy for determining the relative three dimensional coordinates of ground stations within one centimeter over baselines of 20 to 10,000 kilometers is discussed. The system is referred to as 3-D Multilateration and has applications in earthquake hazard assessment, precision surveying, plate tectonics, and orbital mechanics. The accuracy is obtained by using pulsed lasers to obtain simultaneous slant ranges between several ground stations and a moving retroreflector with known trajectory for aiming the lasers

A photogrammetric approach for real-time 3D localization and tracking of pedestrians in monocular infrared imagery

Target tracking within conventional video imagery poses a significant challenge that is increasingly being addressed via complex algorithmic solutions. The complexity of this problem can be fundamentally attributed to the ambiguity associated with actual 3D scene position of a given tracked object in relation to its observed position in 2D image space. We propose an approach that challenges the current trend in complex tracking solutions by addressing this fundamental ambiguity head-on. In contrast to prior work in the field, we leverage the key advantages of thermal-band infrared (IR) imagery for the pedestrian localization to show that robust localization and foreground target separation, afforded via such imagery, facilities accurate 3D position estimation to within the error bounds of conventional Global Position System (GPS) positioning. This work investigates the accuracy of classical photogrammetry, within the context of current target detection and classification techniques, as a means of recovering the true 3D position of pedestrian targets within the scene. Based on photogrammetric estimation of target position, we then illustrate the efficiency of regular Kalman filter based tracking operating on actual 3D pedestrian scene trajectories. We present both a statistical and experimental analysis of the associated errors of this approach in addition to real-time 3D pedestrian tracking using monocular infrared (IR) imagery from a thermal-band camera. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

Enriching the fan experience in a smart stadium using internet of things technologies

Rapid urbanization has brought about an influx of people to cities, tipping the scale between urban and rural living. Population predictions estimate that 64% of the global population will reside in cities by 2050. To meet the growing resource needs, improve management, reduce complexities, and eliminate unnecessary costs while enhancing the quality of life of citizens, cities are increasingly exploring open innovation frameworks and smart city initiatives that target priority areas including transportation, sustainability, and security. The size and heterogeneity of urban centers impede progress of technological innovations for smart cities. We propose a Smart Stadium as a living laboratory to balance both size and heterogeneity so that smart city solutions and Internet of Things (IoT) technologies may be deployed and tested within an environment small enough to practically trial but large and diverse enough to evaluate scalability and efficacy. The Smart Stadium for Smart Living initiative brings together multiple institutions and partners including Arizona State University (ASU), Dublin City University (DCU), Intel Corporation, and Gaelic Athletic Association (GAA), to turn ASU's Sun Devil Stadium and Ireland's Croke Park Stadium into twinned smart stadia to investigate IoT and smart city technologies and applications

Mathematical theory of the Goddard trajectory determination system

Basic mathematical formulations depict coordinate and time systems, perturbation models, orbital estimation techniques, observation models, and numerical integration methods

Impact of multiscale dynamical processes and mixing on the chemical composition of the upper troposphere and lower stratosphere during the Intercontinental Chemical Transport Experiment–North America

We use high-frequency in situ observations made from the DC8 to examine fine-scale tracer structure and correlations observed in the upper troposphere and lower stratosphere during INTEX-NA. Two flights of the NASA DC-8 are compared and contrasted. Chemical data from the DC-8 flight on 18 July show evidence for interleaving and mixing of polluted and stratospheric air masses in the vicinity of the subtropical jet in the upper troposphere, while on 2 August the DC-8 flew through a polluted upper troposphere and a lowermost stratosphere that showed evidence of an intrusion of polluted air. We compare data from both flights with RAQMS 3-D global meteorological and chemical model fields to establish dynamical context and to diagnose processes regulating the degree of mixing on each day. We also use trajectory mapping of the model fields to show that filamentary structure due to upstream strain deformation contributes to tracer variability observed in the upper troposphere. An Eulerian measure of strain versus rotation in the large-scale flow is found useful in predicting filamentary structure in the vicinity of the jet. Higher-frequency (6–24 km) tracer variability is attributed to buoyancy wave oscillations in the vicinity of the jet, whose turbulent dissipation leads to efficient mixing across tracer gradients