On the Method of Determining Onboard Ephemeris Data L1OC and L3OC GLONASS Aimed at Testing Positioning Algorithms in the GNSS Receiver

The paper considers a method of determining GLONASS ephemeris data in the L1OC and L3OC digital form aimed at testing the algorithms of accurate navigation determinations in consumer navigation equipment. The task of determining long-term motion model parameters of a navigation spacecraft is set as nonlinear problem of designing a matching model. This task is unstable and according to the analysis is categorized as incorrect. The application of a traditional least squares method to determine the long-term motion model parameters of a navigation spacecraft does not allow to obtain equally accurate solutions of condition equations system when initial conditions and/ or iterations amount have been changed. In this respect, Tikhonov’s regularization method has been carried out. It is based on the application of additional prior information. The obtained results have been tested by the comparison of estimated navigation spacecraft position according to the adjusted (speed and acceleration) ephemeris data, long-term motion model parameters and SP3 final reference ephemeris published on the SVOEVP website. The long-term motion model parameters of GLONASS navigation spacecraft that were defined by regularizing algorithms, have allowed to calculate the position of navigation spacecraft orbital grouping in terms of four-hours fitting intervals within 0,2 m tolerance (maximum deviations according to module of estimated navigation spacecraft position from SVOEVP SP3 reference ephemeris)

Measured Elevation of Lightning and Aurora in the Jovian Atmosphere

<p>As part of the Juno MAG investigation, each magnetometer features dedicated star trackers providing accurate bias free attitude information continuously throughout the mission. These optical sensors are optimized for low-light scenarios, which enables detection of stars and objects as faint as 7-8Mv. The Juno mission features a highly elliptical polar orbit with a period of ~53 days, with periapsis as close as 3.300km above the cloud tops. In combination with the 13° off pointing of the star tracker cameras from the Juno spin axis in anti-sun direction, the Jovian night side high latitude regions regularly enters the field of regard of these star trackers. This geometry facilitates imaging low light phenomenas as lightning and aurora at a large slanted angle in the upper parts of Jupiter’s atmosphere. The large slant angle enables estimation of the vertical structure, by combining the detections with accurate attitude and spacecraft position information. We present up-to-date images of detected lightning events, visible wavelength aurora and the measured vertical structure, and discuss implications of these measurements for the Jovian atmosphere at the resulting altitudes</p>

Estimation of Visual Shoreline Navigation Errors

In some cases, navigation of aircraft or spacecraft may need to be conducted in a Global Navigation Satellite System (GNSS)-denied environment. So, additional sources of navigation information may need to be used to increase navigation precision and resilience. Such sources can include visual navigation systems such as visual shoreline navigation. The main feature of visual shoreline navigation is the severe variability of navigation errors depending on the shape of the observed shoreline, the distance and the view angle of the observation. Such variations are so great that it is not possible to use average values of errors. So, each measurement of an aircraft or spacecraft position should be accompanied with an estimation of the error covariance matrix in real time. It is proposed to use the Cramer-Rao lower bound of visual shoreline navigation errors as such a matrix. The method for constructing the Cramer-Rao lower bound is described in this paper.

Autonomous visual recognition of known surface landmarks for optical navigation around asteroids

We present an autonomous visual landmark recognition and pose estimation algorithm designed for use in navigation of spacecraft around small asteroids. Landmarks are selected as generic points on the asteroid surface that produce strong Harris corners in an image under a wide range in viewing and illumination conditions; no particular type of morphological feature is required. The set of landmarks is triangulated to obtain a tightly fitting mesh representing an optimal low resolution model of the natural asteroid shape, which is used onboard to determine the visibility of each landmark and enables the algorithm to work with highly concave bodies. The shape model is also used to estimate the centre of brightness of the asteroid and eliminate large translation errors prior to the main landmark recognition stage. The algorithm works by refining an initial estimate of the spacecraft position and orientation. Tests with real and synthetic images show good performance under realistic noise conditions. Using simulated images, the median landmark recognition error is 2m, and the error on the spacecraft position in the asteroid body frame is reduced from 45m to 21m at a range of 2km from the surface. With real images the translation error at 8km to the surface increases from 107m to 119m, due mainly to the larger range and lack of sensitivity to translations along the camera boresight. The median number of landmarks detected in the simulated and real images is 59 and 44 respectively. This algorithm was partly developed and tested during industrial studies for the European Space Agency’s Marco Polo-R asteroid sample return mission.