THE LEAST-SQUARES AMBIGUITY DECORRELATION ADJUSTMENT - ITS PERFORMANCE ON SHORT GPS BASE-LINES AND SHORT OBSERVATION SPANS

The least-squares ambiguity decorrelation adjustment is a method for f ast GPS double-difference (DD) integer ambiguity estimation. The perfo rmance of the method will be discussed, and although it is stressed th at the method is generally applicable, attention is restricted to shor t-baseline applications in the present contribution. With reference to the size and shape of the ambiguity search space, the volume of the s earch space will be introduced as a measure for the number of candidat e grid points, and the signature of the spectrum of conditional varian ces will be used to identify the difficulty one has in computing the i nteger DD ambiguities. It is shown that the search for the integer lea st-squares ambiguities performs poorly when it takes place in the spac e of original DD ambiguities. This poor performance is explained by me ans of the discontinuity in the spectrum of conditional variances. It is shown that through a decorrelation of the ambiguities, transformed ambiguities are obtained which generally have a flat and lower spectru m, thereby enabling a fast and efficient search. It is also shown how the high precision and low correlation of the transformed ambiguities can be used to scale the search space so as to avoid an abundance of u nnecessary candidate grid points. Numerical results are presented on t he spectra of conditional variances and on the statistics of both the original and transformed ambiguities. Apart from presenting numerical results which can typically be achieved, the contribution also emphasi zes and explains the impact on the method's performance of different m easurement scenarios, such as satellite redundancy, single vs dual-fre quency data, the inclusion of code data and the length of the observat ion time span.