Population Fusion Optimization Algorithm for GNSS Integer Ambiguity Resolution Based on PSO and AFAS

Carrier phase measurement is an important way to realize fast and high-precision positioning of Global Navigation Satellite System (GNSS), and accurate solving of whole-week ambiguity is one of the key steps. Particle swarm algorithm (PSO) has fast convergence speed but is easy to fall into local optimum, and artificial fish swarm algorithm (AFSA) has good global optimization performance but slow convergence speed, so a GNSS perimeter ambiguity population fusion optimization algorithm (PSOAF) is proposed by combining the advantages of the two optimization algorithms. First, the floating-point solution of the whole-week ambiguity and the corresponding covariance matrix are solved by the carrier phase double difference equation. Then, the inverse integer Cholesky algorithm is used to down-correlate the floating-point solution of the fuzziness. Next, the convergence and search performance of the algorithm are improved by optimizing the fitness function for the deficiency of integer least squares estimation. Finally, the whole week fuzzy degree is solved by PSOAF algorithm. Classical examples and experimental studies consistently illustrate the PSOAF algorithm's ability to swiftly