Mahmud, Mohd Razali; (1999) Precise three-dimensional attitude estimation from independent GPS arrays. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

This thesis concentrates on the GPS processing necessary to estimate three-dimensional attitude parameters directly from any number of independent antennas connected to their own receivers. In order to achieve this objective, an algorithm has been developed that searches in attitude space using a single epoch of dual frequency data. This has involved modifying the standard double difference phase observation equation to include the attitude parameters. The main advantage of this approach compared to the classical method based on individual baselines is that it increases the redundancy which improves ambiguity resolution and reliability. Moreover, it enables the data involving satellite/antenna combinations contaminated by multipath to be identified and removed from the estimation. In this way, more accurate and robust attitude solutions can be achieved. The results of static investigations using real and simulated data to test the performance of GPS ambiguity resolution and outlier detection under different redundancy conditions have shown that Successful ambiguity resolution (100% success rate) was achieved with a direct attitude system when the random errors introduced were created using a standard deviation of 0.005 m. On the other hand, a single baseline system fails to resolve the ambiguities on some of the epochs. A direct attitude system was able to reject 92% of the total epochs for an error of 0.005 m introduced into LI phase observation on one of the satellites. On the other hand, a single baseline system was only able to reject 69% of the total epochs. Overall, the results of the investigations have shown that the increased redundancy improves the ambiguity resolution, reliability and robustness of the GPS attitude system. Such a method will be useful in high multipath environments where affected observations may be rejected as outliers in the statistical tests in the least squares solutions.

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