Projecte llegit
Títol: Extended Kalman Filter for Interferometric Precise Orbit Determination of Geostationary Satellites
Director/a: NICOLÁS ÁLVAREZ, JORGE
Departament: TSC
Títol: Extended Kalman Filter for Interferometric Precise Orbit Determination of Geostationary Satellites
Data inici oferta: 28-01-2021 Data finalització oferta: 28-09-2021
Estudis d'assignació del projecte:
GR ENG SIST AEROESP
| Tipus: Individual | |
| Lloc de realització: |
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| Segon director/a (UPC): BROQUETAS IBARS, ANTONI | |
| Paraules clau: | |
| interferometry, precise orbit determination, remote sensing, radar, python, geosynchronous satellites | |
| Descripció del contingut i pla d'activitats: | |
| Background: Low Earth Orbit Synthetic Aperture Radars (LEOSAR) present a main drawback regarding their revisit time of several days or weeks. At most, they can only provide an image of the same area of the planet per week. To mitigate this limitation, Geosynchronous Synthetic Aperture Radars (GEOSAR) missions will be able to provide permanent monitoring over wide areas of the planet. GEOSAR presents a main challenge: it requires unprecedented orbit determination precision. We have to demonstrate that we can get this precision before launching any spacecraft. We have designed and built a ground interferometer in the UPC-D3 building in order to track non-cooperative geostationary telecommunication satellites.
The student will work in the context of some on-going remote sensing missions: The European Space Agency (ESA) has selected Hydroterra in the call for its 10th Earth Explorer. Hydroterra will help scientists unravel the details of the daily water cycle. In the event of a natural disaster, they would be able to predict the development of floods and emergency services will be able to safely evacuate the citizens before the water rise. NASA Jet Propulsion Laboratory (JPL) has a keen interest in the presented technique and has contacted us to join their team in an emerging snow remote sensing mission. Goal: The project consists of retrieving geostationary satellite orbits from experimental interferometric measurements. The student will code an Extended Kalman Filter in Python in order to estimate the satellite position. They will work with different datasets of observables: single- satellite and three-satellite tracking. Finally, the uncertainty of the estimated solution with respect to the true position of the satellite must be determined. |
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| Overview (resum en anglès): | |
| Low Earth Orbit Synthetic Aperture Radar (LEOSAR) missions cannot provide continuous monitoring over the same area of the planet. At most, they can only provide an image of the same point of the Earth per week. In order to mitigate this limitation, the Geosynchronous Synthetic Aperture Radar (GEOSAR) concept aims to provide almost permanent monitoring over wide areas of the planet. This work is performed in the context of an on-going GEOSAR mission: Hydroterra will help scientists unravel the details of the daily water cycle. Thereby, in the event of natural hazards, they would be able to continuously monitor the development of floods, landslides or subsidence allowing emergency services to safely evacuate the citizens before the disaster.
GEOSAR presents a main challenge: it requires unprecedented orbit determination precision in order to form properly focused radar images. We have to demonstrate that we can get this precision before launching any spacecraft. For that purpose, a geosynchronous satellite tracking system based on interferometry has been developed. The ground interferometer built consists of three antennas which form compact (10 m) interferometric baselines. They receive the DVB-S TV broadcasting signals from the non-cooperative ASTRA 19.2¿ E geostationary satellite constellation. The relative phases measured between each pair of antennas are used as orbit observables. This experimental interferometric measurements can be used for retrieving geostationary satellite orbits. This project consists of implementing in a Python code a differential correction technique called Extended Kalman Filter (EKF) to the determination process in order to reduce the error. EKF is a recursive predictive algorithm that estimates the state variables (e.g. position and velocity) of a dynamic linearized system. Different tests have been made in order to analyze EKF algorithm performance. After that, results of single-satellite and three-satellite tracking are consistent with the Two Line Element (TLE) sets of the satellites. |
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