Within an ultra-tightly (or deeply) integrated GNSS/INS system, GNSS signal correlation delivers correlator values as input to the integration filter. On the other side, the integration filter controls the correlation process by determining the Numerically Controlled Oscillator (NCO) values. As GNSS signal correlation is a computational trivial but a time-consuming process, we propose for R&D in this area an alternative approach to first generate for each GNSS signal multi-correlator values and store them for the later GNSS/INS filter development work. Once the filter runs, it interpolates from the multi-correlator values the actual needed correlation values. The multi-correlator values thus act like a data compression for the GNSS signals. This paper discusses the mathematical framework for this data compression, which is loosely described as a sufficient statistic. The statistic consists of the correlation values themselves plus the NCO values that have been used during the correlation process. The generation and interpolation process will be described in the paper with all mathematical details, as well as interpolation limits in code phase and Doppler direction. The approach is validated by comparison of Global Positioning System (GPS) C/A code pseudorange and carrier phase data from direct tracking to results from a MATLAB based receiver using the multi-correlator values as sufficient statistics.    «

Within an ultra-tightly (or deeply) integrated GNSS/INS system, GNSS signal correlation delivers correlator values as input to the integration filter. On the other side, the integration filter controls the correlation process by determining the Numerically Controlled Oscillator (NCO) values. As GNSS signal correlation is a computational trivial but a time-consuming process, we propose for R&D; in this area an alternative approach to first generate for each GNSS signal multi-correlato...    »