An effective QR-based reduction algorithm for the fast estimation of GNSS high-dimensional ambiguity resolution

作者：Lu, L.; Liu, W.^*; Zhang, X.

来源：Survey Review, 2018, 50(358): 57-68.

DOI：10.1080/00396265.2016.1230953

• Integer least-squares
• High-dimensional ambiguity
• Fast estimation
• Lattice reduction
• Whole size reduction
• Whole deep insertion

To fast estimate high-dimensional ambiguities, we propose a new lattice reduction algorithm based on QR decomposition, which achieves fast integer transformation through an iterative strategy of whole size reduction and the deep insertion of minimum basis vectors. It acquires better basis vectors for ambiguity resolution. The feasibility of the proposed algorithm is verified by comparing its performance with those of LAMBDA, LLL, the parallel Cholesky-based reduction algorithm with ascending sorting (ASCE), and a modified LLL algorithm with deep insertions (PotLLL) under three experimental scenarios. The Hermite defect and defined length ratio are used to measure the reduction quality. Both metrics verify that our proposed method acquires a good reduced basis for accelerating the ambiguity search. To evaluate the practical ambiguity resolution behaviour, we tracked the runtime of ambiguity resolution. The results show that the computational efficiency of the proposed algorithm is better than those of the comparative algorithms.

• 出版日期2018
• 单位武汉大学