Abstract-Space-based positioning, navigation and timing (PNT) technologies, such as the Global Navigation Satellite Systems (GNSS) provide position, velocity, and timing information to an unlimited number of users around the world. In recent years PNT information has become increasingly critical to the security, safety and prosperity of the World's population, and is now widely recognized as an essential element of the global information infrastructure.Due to its vulnerabilities and line-of-sight requirements GNSS alone is unable to provide PNT with the required levels of integrity, accuracy, continuity and reliability. A multi-sensor navigation approach offers an effective augmentation in GNSSchallenged environments that holds a promise of delivering robust and resilient PNT. Traditionally, sensors such as inertial measurement units (IMUs), barometers, magnetometers, odometers and digital compasses, have been used. However, recent trends have largely focused on image-based, terrain-based and collaborative navigation to recover the user location. This paper offers a review of the technological advances that have taken place in PNT over the last two decades, and discusses various hybridizations of multi-sensory systems, building upon the fundamental GNSS/IMU integration. The most important conclusion of this study is that in order to meet the challenging goals of delivering continuous, accurate and robust PNT to the ever-growing numbers of users, the hybridization of a suite of different PNT solutions is required.

We develop a method that allows a flyer to estimate its own motion (egomotion), the wind velocity, ground slope, and flight height using only inputs from onboard optic flow and air velocity sensors. Our artificial algorithm demonstrates how it could be possible for flying insects to determine their absolute egomotion using their available sensors, namely their eyes and wind sensitive hairs and antennae. Although many behaviors can be performed by only knowing the direction of travel, behavioral experiments indicate that odor tracking insects are able to estimate the wind direction and control their absolute egomotion (i.e., groundspeed). The egomotion estimation method that we have developed, which we call the opto-aeronautic algorithm, is tested in a variety of wind and ground slope conditions using a video recorded flight of a moth tracking a pheromone plume. Over all test cases that we examined, the algorithm achieved a mean absolute error in height of 7% or less. Furthermore, our algorithm is suitable for the navigation of aerial vehicles in environments where signals from the Global Positioning System are unavailable.

Index Terms-Global positioning system (GPS) receiver, partitioned subspace projection, self-interference mitigation, weak signal acquisition.

No abstract

No abstract