Post-processed accuracy prediction for GNSS positioning

Details Post-processed accuracy prediction for GNSS positioning Post-processed accuracy prediction for GNSS positioning

2011-07-19

2011-07-19

Tarcza, Thomas H (Department: 3662)

Communications: directive radio wave systems and devices (e.g.,

Directive

Including a satellite

C342S357230, C342S357270, C342S357720

Reexamination Certificate

active

07982667

ABSTRACT:
Methods and apparatus for processing of data from GNSS receivers are presented. (1) A real-time GNSS rover-engine, a long distance multi baseline averaging (MBA) method, and a stochastic post-processed accuracy predictor are described. (2) The real-time GNSS rover-engine provides high accuracy position determination (decimeter-level) with short occupation time (2 Minutes) for GIS applications. The long distance multi baseline averaging (MBA) method improves differential-correction accuracy by averaging the position results from several different baselines. This technique provides a higher accuracy than any single baseline solution. It was found, that for long baselines (more than about 250 km), the usage of non-iono-free observables (e.g. L1-only or wide-lane) leads to a higher accuracy with MBA compared to the commonly used iono-free (LC) combination, because of the less noisy observables and the cancellation of the residual ionospheric errors. (3) The stochastic post-processed accuracy (SPPA) predictor calculates during data collection an estimate of the accuracy likely to be achieved after post-processing. This helps to optimize productivity when collecting GNSS data for which post-processed accuracy is important. The predictor examines the quality of carrier measurements and estimates how well the post-processed float solution will converge in the time since carrier lock was obtained.

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