by Inder J. Gupta, The Ohio State University
It is well known that antennas can cause biases in code phase and carrier phase measurements in GNSS receivers, and these biases are aspect dependent in that the biases vary from one satellite to the next in view of a GNSS receiver. This results in errors in the position and time solutions. Fixed reception pattern GNSS antennas can be calibrated for these biases in GNSS measurements. The same is not true for adaptive antennas which are needed for electronics protection in GNSS receivers. In this short course, we will describe the latest methods to estimate and mitigate adaptive antenna induced biases in GNSS receivers. The methods will include optimum filtering in GNSS antenna electronics as well as modification of GNSS receivers. Methods to include the platform effects in computer simulations and wave front simulators will also be discussed. The platform of interest will include rotor crafts.
by Michael Veth, Air Force Institute of Technology
This tutorial provides an introduction to alternative navigation techniques for navigation in GPS denied environments such as urban indoor and outdoor scenarios. The tutorial's primary focus is on the use of passive Electro-optical (EO) sensors such as vision cameras to aid an Inertial Navigation System (INS) and provide navigation performance similar to GPS. The discussion includes the basic principles of EO sensor integration with an INS; the feature-based techniques and optical-flow-based; feature extraction and tracking algorithms; and, the basics of integrated EO/INS mechanizations. A feature-based passive aiding method is addressed in details. In this case, the integration is performed using a tightly coupled Kalman filter. EO data are applied to estimate inertial drift terms in order to mitigate the drift in inertial navigation outputs. Inertial data are applied for robust feature matching. Experimental data collected in actual indoor environments are applied to demonstrate performance of EO/INS integrated approach. Prerequisites: Familiarity with basic Kalman filter and INS principles.
by Dorota Grejner-Brzezinska, The Ohio State University
This short course will discuss the design, implementation and the performance evaluation of a personal navigator prototype, which integrates GPS, IMU, digital barometer, magnetometer, and human pedometry to facilitate navigation and tracking of military and rescue ground personnel, developed at The Ohio State University Satellite Positioning and Inertial Navigation (SPIN) Laboratory. The goal of this system is to provide precise and reliable position/velocity/heading information of the individuals in various environments. In the open sky environment, either GPS alone, or a GPS/IMU system can facilitate the basic navigation functionality with the accuracy depending on the choice of GPS and IMU sensors. In confined and GPS-denied environments, however, the main challenge for a personal navigator is to implement a backup plan to maintain the navigation information in the absence of GPS signals.
A basic human locomotion model, considered as navigation sensor, works with the step length (SL) and step direction (SD) as primary parameters, which are adaptively estimated by a machine learning system. The major focus of this workshop will be on dead reckoning (DR) navigation supported by human dynamics during GPS signal outages. It is demonstrated that in the absence of GPS signals, the sensors used in the current prototype can sense the body locomotion in terms of its dynamics and geometry that represent an implicit function of SL and SD. The implementation of the DR system based on human dynamics is based on a combination of Fuzzy Logic (FL) and Artificial Neural Network (ANN), integrated into a Knowledge-Based System (KBS). The knowledge-based system is trained a priori using sensory data collected by various operators in various environments during good GPS signal reception, and is used to support navigation under GPS-denied conditions.
by Jade Morton, Miami University of Ohio
Ionosphere causes the largest error variable in GPS code and carrier phase measurements. This course will present the basic characteristics of the ionosphere structure, the nature of its complex refractive index, the code phase delay, carrier phase advance, and Doppler shift caused by signal refraction, amplitude and phase scintillation caused by signal diffraction, signal distortion caused by the dispersive medium, and signal bending. Current techniques that correct first order ionosphere error and assessment of higher order error will also be discussed.
by Frank van Graas and Maarten Uijt de Haag, Ohio UniversityIntegration of GPS carrier phase with inertial measurements. Overview of principles and techniques to combine cm-level integrated GPS carrier phase measurements with inertial delta-angles and delta-velocities. Topics to be covered include mechanization equations, accurate GPS delta position from carrier phase, batch versus sequential processing, time synchronization, lever arm correction, and fault detection.