Handling Temporal Correlated Noise in Large Scale

Transcript:Handling Temporal Correlated Noise in Large Scale:
Handling Temporal Correlated Noise in Large Scale Global GNSS processing Patrick Dumitraschkewitz, Torsten Mayer-Guerr IGS Workshop 2024 1.07.2024 2 Motivation Station GRAZ post-fit residuals of L1C of the GPS satellites PRN G01 and G03 in the time domain (left) and the frequency domain (right) https://github.com/groops-devs Dumitraschkewitz, Mayer-Guerr Raw observation approach Key concept Use all available observations… … as they are observed by the receiver… No linear combinations or differences … in a common least squares adjustment. Observation Corrections Antenna center offsets/variations Phase wind-up Relativistic time correction … Clock errors (Recv., Trans.) Tropospheric influence Ionospheric influence Phase biases (Recv., Trans.) Ambiguity Code biases (Recv., Trans.) 3 Dumitraschkewitz, Mayer-Guerr 4 LSA estimation global GNSS observation equation C1C C1W C2W L1C L1W L2W obs. epoch 0 epoch 1 … epoch i receivers transmitters ambiguities clocks Dumitraschkewitz, Mayer-Guerr 5 Normal equation matrix structure Normal equation matrix structure of the global GNSS network processing using raw observation approach. Dumitraschkewitz, Mayer-Guerr Requirements of the stochastic model The stochastic model is to be used on distributed memory system and it shall not excessively use communication between the processes The stochastic model shall not bloat the normal equation matrix nor should it lead to a fully populated normal equation matrix 333093 parameters for 178 stations to be estimated With diagonal covariance matrix requires ~100 GB of RAM The stochastic model must be applicable in block wise normal equation accumulation 6 Normal equation matrix structure of the global GNSS network processing using raw observation approach. Dumitraschkewitz, Mayer-Guerr 7 GNSS Stochastic Modelling – Temporal covariance models Three general ways to estimate VC components Covariance component estimation Iterative variance component estimation (VCE) , Minimum norm quadratic unbiased estimation (MINQUE, Wang et al. 1998) LSA-VCE, (Teunissen and Amiri-Simkooei 2008) Turbulence theory and correlations caused by tropospheric refractions (Kermarrec and Schön 2014) Post fit residual fitting Stochastic processes fitted to post fit residuals Autoregressive-moving average (ARMA) processes (Luo et al. 2012) Method of interest Dumitraschkewitz, Mayer-Guerr 8 Decorrelating temporal correlations for receiver/transmitter pairs Dumitraschkewitz, Mayer-Guerr 9 AR(MA) processes, decorrelation and covariance matrices Dumitraschkewitz, Mayer-Guerr 10 AR(P) process in global GNSS processing AR(P) process add P diagonal elements in the epochwise parameters Receivers Satellites Global Ambiguities Epoch i Dumitraschkewitz, Mayer-Guerr 11 AR(P) process in global GNSS processing AR(P) process add P diagonal elements in the epochwise parameters Receivers Satellites Global Ambiguities Epoch i AR(1) Dumitraschkewitz, Mayer-Guerr 12 AR(P) process in global GNSS processing Receivers