Based on the structural characteristics of the double-differenced normal equation, a new method was proposed to resolve the ambiguity float solution through a selection of parameter weights to construct an appropriate...Based on the structural characteristics of the double-differenced normal equation, a new method was proposed to resolve the ambiguity float solution through a selection of parameter weights to construct an appropriate regularized matrix, and a singular decomposition method was used to generate regularization parameters. Numerical test results suggest that the regularized ambiguity float solution is more stable and reliable than the least-squares float solution. The mean square error matrix of the new method possesses a lower correlation than the variancecovariance matrix of the least-squares estimation. The size of the ambiguity search space is reduced and the search efficiency is improved. The success rate of the integer ambiguity searching process is improved significantly when the ambiguity resolution by using constraint equation method is used to determine the correct ambiguity integervector. The ambiguity resolution by using constraint equation method requires an initial input of the ambiguity float solution candidates which are obtained from the LAMBDA method in the new method. In addition, the observation time required to fix reliable integer ambiguities can he significantly reduced.展开更多
The ambiguity resolution in the field of GPS is investigated in detail. A new algorithm to resolve the ambiguity is proposed. The algorithm first obtains the floating resolution of the ambiguity aided with triple diff...The ambiguity resolution in the field of GPS is investigated in detail. A new algorithm to resolve the ambiguity is proposed. The algorithm first obtains the floating resolution of the ambiguity aided with triple difference measurement. Decorrelation of searching space is done by reducing the ambiguity covariance matrix＇s dimension to overcome the possible sick factorization of the matrix brought by Z-transformation. In simulation, the proposed algorithm is compared with least-squares ambiguity decorrelation adjustment （LAMBDA）. The result shows that the proposed algorithm is better than LAMBDA because of lesser resolving time, which approximately reduces 20% resolving time. Thus, the proposed algorithm adapts to the high dynamic real-time applications.展开更多
文摘Based on the structural characteristics of the double-differenced normal equation, a new method was proposed to resolve the ambiguity float solution through a selection of parameter weights to construct an appropriate regularized matrix, and a singular decomposition method was used to generate regularization parameters. Numerical test results suggest that the regularized ambiguity float solution is more stable and reliable than the least-squares float solution. The mean square error matrix of the new method possesses a lower correlation than the variancecovariance matrix of the least-squares estimation. The size of the ambiguity search space is reduced and the search efficiency is improved. The success rate of the integer ambiguity searching process is improved significantly when the ambiguity resolution by using constraint equation method is used to determine the correct ambiguity integervector. The ambiguity resolution by using constraint equation method requires an initial input of the ambiguity float solution candidates which are obtained from the LAMBDA method in the new method. In addition, the observation time required to fix reliable integer ambiguities can he significantly reduced.
文摘The ambiguity resolution in the field of GPS is investigated in detail. A new algorithm to resolve the ambiguity is proposed. The algorithm first obtains the floating resolution of the ambiguity aided with triple difference measurement. Decorrelation of searching space is done by reducing the ambiguity covariance matrix＇s dimension to overcome the possible sick factorization of the matrix brought by Z-transformation. In simulation, the proposed algorithm is compared with least-squares ambiguity decorrelation adjustment （LAMBDA）. The result shows that the proposed algorithm is better than LAMBDA because of lesser resolving time, which approximately reduces 20% resolving time. Thus, the proposed algorithm adapts to the high dynamic real-time applications.
