An Improved PSO Algorithm and Its Application in GNSS Ambiguity Resolution

Abstract

In this study, we proposed a novel method for global navigation satellite system (GNSS) ambiguity resolution (AR). The proposed method utilizes an improved particle swarm optimization (IPSO) algorithm to obtain the GNSS integer ambiguity with the double differenced (DD) float resolution and its corresponding covariance matrix. First, we introduced population maturity to the standard PSO (SPSO) algorithm for the adaptive adjustment of inertia weight. Next, to improve the global convergence and robustness of the SPSO algorithm, we adopted population classification and constructed a Gauss mutation for the particle evolution process of the optimal population. Then, we applied the IPSO algorithm in the field of GNSS AR, called IPSO–AR. Finally, we evaluated the performance of the IPSO–AR algorithm under different DD ambiguity float resolutions with various dimensions and precisions. Numerical results showed that compared with the SPSO–AR algorithm, the IPSO–AR algorithm has a superior correct rate, but low efficiency. Under the appropriate parameter settings, the efficiency of the IPSO–AR algorithm is mainly dependent on the dimensions of DD ambiguity, whereas the correct rate of the IPSO–AR algorithm is mainly dependent on the precision of DD ambiguity. The proposed IPSO–AR algorithm has potential applications under the conditions of few visible satellites or constrained baseline length.

Keywords

• global navigation satellite system (GNSS)
• ambiguity resolution (AR)
• particle swarm optimization (PSO)