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针对整周模糊度解算中实时性不强的问题,提出了一种基于实数编码的自适应遗传算法。首先利用卡尔曼滤波求解整周模糊度的浮点解,然后采用排序和多次(逆)双乔里斯基分解对浮点解及其协方差阵进行降相关处理,降低整周模糊度各分量之间的相关性,最后利用已知基线确定模糊度搜索空间,基于实数编码,将自适应遗传算法应用在整周模糊度的搜索解算过程中,求得整周模糊度的最优解。仿真结果表明,本文算法与Lambda算法相比,耗时更少;与简单遗传算法相比,具有更强的收敛能力,是一种高效的整周模糊度快速解算方法。
Aiming at the problem of the lack of real-time performance in integer ambiguity resolution, an adaptive genetic algorithm based on real number encoding is proposed. Firstly, Kalman filter is used to solve the floating-point solution of the whole-week ambiguity. Then the ordering and multi-order (inverse) biharly-Richardsis decomposition are used to reduce the floating-point solution and its covariance matrix, Finally, we use the known baseline to determine the ambiguity search space. Based on the real number coding, the adaptive genetic algorithm is applied to the whole ambiguity search process, and the optimal ambiguity resolution is obtained. The simulation results show that the proposed algorithm is less time-consuming than Lambda algorithm and has better convergence ability than the simple genetic algorithm. It is an efficient method to solve the ambiguity of integer weeks.