TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
BIOMASS_E2ES_IGARSS2011.ppt
1. BIOMASS End-to-End Mission Performance Simulator Paco López-Dekker , Francesco De Zan, Thomas Börner, Marwan Younis, Kostas Papathanassiou (DLR); Tomás Guardabrazo (DEIMOS); Valerie Bourlon, Sophie Ramongassie, Nicolas Taveneau (TAS-F); Lars Ulander, Daniel Murdin (FOI); Neil Rogers, Shaun Quegan (U. Sheffiled) and Raffaella Franco (ESA) Microwaves and Radar Institute, German Aerospace Center (DLR)
13. Review of PGM algorithm Generation of interferometric/polarimetric channels for the scatter (correlated) and the noise (uncorrelated) Spectral shift modulation (geometric decorrelation part I) 2-D convolution Add ionospheric phase screen (scintillations) and Faraday rotation Spectral shift demodulation (geometric decorrelation part II) Ambiguity stacking Additional system disturbances (cross-talk, phase and gain drifts…) L1b product generation (multilooking) L1a product generation SGM, OSS GM OSS GM IM, GM OSS OSS ICM GM inputs macro steps
14. Multichannel signal simulation Channel Linear Combination channel #1 channel #2 channel #N channel #1 channel #2 channel #N channel #1 channel #2 channel #N Independent channels (complex) Correlated channels (complex) Spatial convolutions Desired spectral properties for each complex channel Tree Height Coherence – HH-HH SLC – HH
15. Introduction of Ionospheric distorion Orbit Target 1 Aperture angle: This is what really matters! Lower (virtual) orbit Equivalent Aperture Target 2 Ionosphere (modeled as a layer) This part of the ionosphere Modifies this part of the raw data for Target 1 … but this part for Target 2 Ionospheric distortion cannot be applied directly to raw data!!! (the raw data distortion is target dependent) For an orbit at Ionosphere height Distortions can be applied directly to the raw data Aperture length