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Abstract
Global Navigation Satellite System (GNSS) carrier phase ambiguity resolution (AR) is the key technique to high precision positioning and navigation. Ant colony optimisation (ACO) as a stochastic meta-heuristic method solves combinatorial optimisation problems by construsting solutions iteratively using a colony of ants guided by pheromone trails and heuristic information. This paper seeks to explore the effectiveness of ACO to deal with the AR problem and closest lattice point problem. The performance of this new method is evaluated considering several simulated examples with different dimensions. The results show that the proposed algorithm can compete efficiently with other promising approaches to the problem and provide integer optimal solutions in often simulated scenarios. We hope that this paper provides a starting point for researches in applying ACO algorithm and other stochastic methods in the AR problem and other GNSS problems due to the simplicities involved in algebraic manipulation.
The authors are very grateful to anonymous reviewers for their constructive comments which significantly improved the presentation and quality of this paper. Thanks also go to Martin Schlueter for helping us understand the ACO algorithm and for his invaluable and very interesting paper. The authors would also like to acknowledge Krzysztof Socha for providing us with some codes in R language and for his invaluable works.