Alternative Methods to Standby Gain Scheduling Following Air Data System Failure

The United States Air Force has advanced fighter aircraft that lose the ability to operate in a large portion of their operating flight envelope when an air data system failure is experienced. These aircraft are reverted to a fixed set of standby-gains that limit their maneuverability, degrade handling qualities, and increase susceptibility to departure. The purpose of this research was to determine if three alternative methods of standby-gain-scheduling could provide robust control with minimal performance degradation despite the lack of air data. To accomplish this, three methods of standby-gain-scheduling were developed, integrated, and tested in the Infinity Cube simulator at the Air Force Research Laboratory/RBCD building. The first method improved upon an algorithm which used inertial data to estimate an aircraft\u27s true velocity used to drive the gains in an F-16 controller. This algorithm was validated by post-processing high-fidelity simulator data and actual flight data. The second method simply used inertial velocities to drive the gains in an F-16 controller. The final method used a disturbance observer controller which controlled aircraft dynamics without the use of gain-scheduling. The results showed the potential for effective aircraft control with minimal performance degradation following an air data system failure. Potential benefits to this research include eliminating the need to make switch actuations to correctly schedule the standby-gains; improving aircraft performance when flying with standby-gains; allowing the pilot to continue with a combat mission instead of returning to base with an air data system failure; and helping contribute to the removal of Pitot tubes in an attempt to eliminate a failure mode and to reduce the radar cross section of an aerial vehicle

PinMe: Tracking a Smartphone User around the World

With the pervasive use of smartphones that sense, collect, and process valuable information about the environment, ensuring location privacy has become one of the most important concerns in the modern age. A few recent research studies discuss the feasibility of processing data gathered by a smartphone to locate the phone's owner, even when the user does not intend to share his location information, e.g., when the Global Positioning System (GPS) is off. Previous research efforts rely on at least one of the two following fundamental requirements, which significantly limit the ability of the adversary: (i) the attacker must accurately know either the user's initial location or the set of routes through which the user travels and/or (ii) the attacker must measure a set of features, e.g., the device's acceleration, for potential routes in advance and construct a training dataset. In this paper, we demonstrate that neither of the above-mentioned requirements is essential for compromising the user's location privacy. We describe PinMe, a novel user-location mechanism that exploits non-sensory/sensory data stored on the smartphone, e.g., the environment's air pressure, along with publicly-available auxiliary information, e.g., elevation maps, to estimate the user's location when all location services, e.g., GPS, are turned off.Comment: This is the preprint version: the paper has been published in IEEE Trans. Multi-Scale Computing Systems, DOI: 0.1109/TMSCS.2017.275146

Optimum Utilization of Positioning Data in SDS III

A new, computerized hydrographic data acquisition and processing system, Shipboard Data System III (SDS III), is being designed and built for use by the National Ocean Service. An integrated positioning and navigation system is a critical element of this development. Design features include the ability to benefit from time-deskewed multiple lines of position from mixed sensor types (both electronic and manual), difficult geometries, and the use of auxiliary speed and heading data in the application of advanced filtering and smoothing techniques for reduction of random measurement noise and recognition of bias errors. Results are highly accurate, stable, and robust. Measurement noise can be reduced by as much as a factor of three without adding significant biases, even on turns, while retaining actual random vessel motions. Operations can continue during complete losses of positioning data for limited but significant periods of time, including during maneuvers

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Invariant EKF Design for Scan Matching-aided Localization

Localization in indoor environments is a technique which estimates the robot's pose by fusing data from onboard motion sensors with readings of the environment, in our case obtained by scan matching point clouds captured by a low-cost Kinect depth camera. We develop both an Invariant Extended Kalman Filter (IEKF)-based and a Multiplicative Extended Kalman Filter (MEKF)-based solution to this problem. The two designs are successfully validated in experiments and demonstrate the advantage of the IEKF design

Sensor Fused Indoor Positioning Using Dual Band WiFi Signal Measurements

A ubiquitous and accurate positioning system for mobile devices is of great importance both to business and research due to the large number of applications and services it enables. In most outdoor environments this problem was solved by the introduction of the Global Positioning System (GPS). In indoor or suburban areas however, the GPS signals are often too weak to enable a reliable position estimate. Instead, other techniques must be utilized to provide accurate positioning. One of these is trilateration based on WiFi signal strengths. This is an auspicious technology to use partly because of the large number of access points (APs) in our everyday environment, and partly due to the possibility of measuring signal strength with a normal smartphone. The technique is further enabled by the move to include transmitters at 2.4 as well as 5 GHz in modern APs, providing a better basis for accurate position estimations. Furthermore, the motion sensors present in today’s smartphones are accurate enough to provide a short-time estimate of the user’s movement with high accuracy. In this thesis, both of these technologies are used to develop an accurate method for indoor positioning, and the contributions can be summed up into two points. The first contribution is an investigation of the behavior of two WiFi frequencies, 2.4 and 5 GHz, where their time dependent noise is proven to be almost uncorrelated with each other. This is then exploited to develop aWiFi-only trilateration algorithm by the use of a particle filter (PF), where the only restriction is that the locations of the APs need to be known. The second contribution is adding an accelerometer and a gyroscope to the algorithm, to provide a more accurate estimation. A step counter is developed using the accelerometer, and the gyroscope detects changes in heading while the WiFi signal strengths give information about the position. This makes it possible to alongside the position also estimate both heading and step length, while still keeping the only restriction of knowing the AP locations. The resulting algorithm produces position estimates with a mean error less than two meters for a specific use case, and around three meters when a more lenient user behavior is allowed