Algorithm for Geodetic Positioning Based On Angle-Of-Arrival of Automatic Dependent Surveillance-Broadcasts

This paper develops a non-precision, three-dimensional, geodetic positioning algorithm for airborne vehicles. The algorithm leverages the proliferation of Automatic Dependent Surveillance – Broadcast (ADS-B) equipped aircraft, utilizing them as airborne navigation aids to generate an RF Angle-of-Arrival (AOA) and Angle-of-Elevation (AOE) based geodetic position. The resulting geodetic position can serve as a redundant navigation system for use during locally limited Global Navigation Satellite System (GNSS) availability, be used to validate on-board satellite navigation systems in an effort to detect local spoofing attempts, and be used to validate ADS-B position reports. The navigation algorithm is an implementation of an Extended Kalman Filter (EKF) that is loosely based on Simultaneous Localization and Mapping (SLAM), in that it tracks ADS-B capable aircraft while simultaneously determining the geodetic position and velocity of the host vehicle. Unlike SLAM, where the absolute location – latitude/longitude – of the landmarks is unknown and must be estimated as the vehicle encounters them, the absolute position of the airborne navigation aids is typically well-known and periodically reported in the ADS-B data set. Because the absolute position of the navigation aids are known, the resulting host vehicle position will also be an absolute, rather than a relative position. Secondarily, the continuous tracking of the airborne navigation aids allows reported ADS-B positions to be validated against the estimated navigation aid position; thereby, concurrently accomplishing ADS-B validation and host vehicle geolocation. This research has demonstrated through a series of simulated Monte-Carlo tests that the algorithm is capable of generating valid position estimates, along with a reliable estimate of its accuracy, across a variety of anticipated input conditions. With multiple GNSS quality navigation aids available, mean position errors below 225 meters were observed. As the quality of the navigation aids decreased, so too did the accuracy of the algorithm. Utilizing navigation aids with an accuracy of 4 nautical miles (95% containment) resulted in mean position errors on the order of 0.75 nautical miles. These results demonstrate that the method is feasible, and even under worst case conditions, the accuracy of the position estimate generated by the algorithm was sufficient to allow an aircraft to navigate to its destination

Theoretical Limits of Lunar Vision Aided Navigation with Inertial Navigation System

The precision navigation capabilities of the Global Positioning System (GPS) are used extensively within US military operations. However, GPS is highly vulnerable to intentional and unintentional external interference. Therefore, a need exists to develop a non-GPS precision navigation method to operate in GPS degraded environments. This research effort presents the theoretical limits of a precision navigation method based on an inertial navigation system (INS) aided by angle measurements with respect to lunar surface features observed by a fixed camera. To accomplish this task, an extended Kalman filter (EKF) was implemented to estimate INS drift errors and bring in simulated lunar feature angle measurements to correct error estimates. The research scope focused solely on the feasibility of lunar vision aided navigation with INS where only measurement noise effects from a simulated CCD camera and barometer were considered. Various scenarios based on camera specifications, lunar feature quantity, INS grade, and lunar orbital parameters were conducted to observe the INS drift correction by lunar feature angle measurements. The resulting trade spaces presented by the scenarios showed theoretical substantial improvement in the navigation solution with respect to a stand alone INS

Method and apparatus for autonomous, in-receiver prediction of GNSS ephemerides

Methods and apparatus for autonomous in-receiver prediction of orbit and clock states of Global Navigation Satellite Systems (GNSS) are described. Only the GNSS broadcast message is used, without need for periodic externally-communicated information. Earth orientation information is extracted from the GNSS broadcast ephemeris. With the accurate estimation of the Earth orientation parameters it is possible to propagate the best-fit GNSS orbits forward in time in an inertial reference frame. Using the estimated Earth orientation parameters, the predicted orbits are then transformed into Earth-Centered-Earth-Fixed (ECEF) coordinates to be used to assist the GNSS receiver in the acquisition of the signals. GNSS satellite clock states are also extracted from the broadcast ephemeris and a parameterized model of clock behavior is fit to that data. The estimated modeled clocks are then propagated forward in time to enable, together with the predicted orbits, quicker GNSS signal acquisition

Low cost inertial-based localization system for a service robot

Dissertation presented at Faculty of Sciences and Technology of the New University of Lisbon to attain the Master degree in Electrical and Computer Science EngineeringThe knowledge of a robot’s location it’s fundamental for most part of service robots. The success of tasks such as mapping and planning depend on a good robot’s position knowledge. The main goal of this dissertation is to present a solution that provides a estimation of the robot’s location. This is, a tracking system that can run either inside buildings or outside them, not taking into account just structured environments. Therefore, the localization system takes into account only measurements relative. In the presented solution is used an AHRS device and digital encoders placed on wheels to make a estimation of robot’s position. It also relies on the use of Kalman Filter to integrate sensorial information and deal with estimate errors. The developed system was testes in real environments through its integration on real robot. The results revealed that is not possible to attain a good position estimation using only low-cost inertial sensors. Thus, is required the integration of more sensorial information, through absolute or relative measurements technologies, to provide a more accurate position estimation

Monocular Vision Localization Using a Gimbaled Laser Range Sensor

There have been great advances in recent years in the area of indoor navigation. Many of these new navigation systems rely on digital images to aid an inertial navigation estimates. The Air Force Institute of Technology (AFIT) has been conducting research in this area for a number of years. The image-aiding techniques are centered around tracking stationary features in order to improve inertial navigation estimates. Previous research has used stereo vision systems or terrain constraints with monocular systems to estimate feature locations. While these methods have shown good results, they do have drawbacks. First, as unmanned exploration vehicles become smaller in size the distance available to create a baseline between two cameras decreases resulting in a decrease of distancing accuracy. Second, if using a monocular system, terrain data might not be known in an unexplored environment. This research explores the use of a small gimbaled laser range sensor and monocular camera to estimate feature locations. The gimbaled system consists of a commercial off-the-shelf range sensor, a pair of hobby-style servos, and a micro controller that accepts azimuth and elevation commands. The system is approximately 15x8x12 cm and weighs less than 120 grams. This novel approach, called laser-aided image inertial navigation, provides precise depth measurements to key features. The location of these key features are then calculated based on the current state estimates of an Extended Kalman filter. This method of estimating feature locations is tested both by simulation and real world imagery. Navigation experiments are presented which compare this method with previous image-aided filters. While only a limited number of tests were conducted, simulated and real world flight tests show that the monocular laser-aided filter can accurately estimate the trajectory of a vehicle to within a few tenths of a meter. This is done without terrain constraints or any prior knowledge of the operational area

Ares I-X Best Estimated Trajectory Analysis and Results

The Ares I-X trajectory reconstruction produced best estimated trajectories of the flight test vehicle ascent through stage separation, and of the first and upper stage entries after separation. The trajectory reconstruction process combines on-board, ground-based, and atmospheric measurements to produce the trajectory estimates. The Ares I-X vehicle had a number of on-board and ground based sensors that were available, including inertial measurement units, radar, air-data, and weather balloons. However, due to problems with calibrations and/or data, not all of the sensor data were used. The trajectory estimate was generated using an Iterative Extended Kalman Filter algorithm, which is an industry standard processing algorithm for filtering and estimation applications. This paper describes the methodology and results of the trajectory reconstruction process, including flight data preprocessing and input uncertainties, trajectory estimation algorithms, output transformations, and comparisons with preflight predictions

Sensor Fusion of Raw GPS Measurements for Autonomous Vehicle Localization

We developed a software able to establish geometric constraints for a localization problem from raw GPS measurements. Then we integrated it in Wolf, a software framework for managing SLAM, enriching its sensor fusion capabilities. In the end we tested the sensor fusion between raw GPS and odometr

6-DoF Navigation Systems for Autonomous Underwater Vehicles

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