Search Results (2)

In GNSS, a double-difference carrier phase observation model is typically employed, and high-accuracy position coordinates can be obtained by resolving the integer ambiguity within the model through algorithmic processing. To address the challenge of a double-difference integer ambiguity resolution, an enhanced Simulated Annealing Ant Lion Optimizer (SAALO) is proposed. This algorithm is designed to efficiently resolve integer ambiguities. First, the performance of the SAALO algorithm was evaluated by comparing its solving speed and success rate with those of the Ant Lion Optimization Algorithm (ALO), the LAMBDA algorithm and the MLAMBDA algorithm. The results demonstrate that the SAALO algorithm achieved a solution success rate that was 0.0496 s and 0.01 s faster than the LAMBDA and M-LAMBDA algorithms, respectively. Second, to further validate the high-dimensional ambiguity resolution capability of the SAALO algorithm, integer ambiguity resolution tests were conducted in both 6-dimensional and 12-dimensional scenarios. The results indicate that the SAALO algorithm achieves a success rate exceeding 98%, confirming its robust performance in high-dimensional problem-solving. Finally, the practical application of the SAALO algorithm was tested in short- and medium-baseline scenarios using a single-frequency GPS system. With a baseline length of 42.7 km, the SAALO algorithm exhibited a slightly faster average solution time compared to the LAMBDA algorithm, while its solution success rate was 5.2% higher. These findings underscore the effectiveness and reliability of the SAALO algorithm in real-world GNSS applications. Full article

Deformation monitoring of engineering structures using the advanced Global Navigation Satellite System (GNSS) has attracted research interest due to its high-precision, constant availability and global coverage. However, GNSS application requires precise coordinates of points of interest through quick and reliable resolution of integer ambiguities in carrier phase measurements. Conventional integer ambiguity resolution algorithms have been extensively researched indeed in the past few decades, although the application of GNSS to structural health monitoring is still limited. In particular, known a priori information related to the structure of a body of interest is not normally considered. This study proposes a composite strategy that incorporates modified least-squares ambiguity decorrelation adjustment (MLAMBDA) method with priori information of the structural deformation. Data from the observation sites of Baishazhou Bridge are used to test method performance. Compared to MLAMBDA methods that do not consider priori information, the ambiguity success rate (ASR) improves by 20% for global navigation satellite system (GLONASS) and 10% for Multi-GNSS, while running time is reduced by 60 s for a single system and 180 s for Multi-GNSS system. Experimental results of Teaching Experiment Building indicate that our constrained MLAMBDA method improves positioning accuracy and meets the requirements of structural health monitoring, suggesting that the proposed strategy presents an improved integer ambiguity resolution algorithm. Full article