Software Development: Massive, Rapid Network Processing with Ambiguity Resolution Geoff Blewitt

Problem Desirable to include as many stations as possible to define SNARF Desirable to produce one unique, rigorous solution with ambiguity resolution PPP is fast, linear ~N (<10 sec/station/cpu) But ambiguity resolution is slow (~N 4 )  Typically limited to ~50 station clusters  Cluster processing not rigorous, not convenient Can ambiguity be made linear and yet rigorous?

Solution Ambizap algorithm – input is PPP solution Solves ambiguities for N-1 neighboring pairs  So is linear ~N, (< 5 sec/station/cpu) Does not count data twice Agrees with full network ambiguity resolution  To << 1 mm rms in station coordinates Implemented for cluster processing 700 station network resolved in ~1 hour/cpu. 1,000,000 rinex files in 2 days on 44-cpu cluster

Progress on Analysis :  IGS  BARGEN  SCIGN  PANGA  EBRY  EUREF  NEARNET (240 station semi-continuous network)  PPP + ambizap takes 7 days on 44-cpu cluster

Mid-Term Prospects UNR solution for SNARF  See preliminary solution by Kreemer et al.  Still need to carefully screen time series Ambizap in GIPSY  In collaboration with JPL, ambizap is being implemented in future distribution of GIPSY (where ambizap follows PPP performed by “gd2p.pl”)  Can be implemented on ~4K cpu Caltech cluster  No practical limit to number of stations (e.g., could easily be tens of thousands per day).

Long-Term Prospects PPP does not improve orbits  So does not improve global-scale parameters Implement ambizap into global IGS processing  Goal: one consistent global-scale solution  Orbit determination using ~1,000 stations, with ambiguity resolution (carrier range)! This in turn will improve PPP, and so on.  Possibly an iterative solution to this. Preliminary scheme has been designed. Will lead to improved reference frames.