一种用于DGPS整周模糊度解算的混合策略麻雀搜索算法

A Hybrid Strategy Sparrow Search Algorithm for DGPS Integer Ambiguity Resolution

针对麻雀搜索算法(SSA)在计算差分全球定位系统(DGPS)整周模糊度过程中出现的全局搜索能力弱、易陷入局部最优等问题,提出了混合策略麻雀搜索算法(HSSSA)。首先,通过引入Circle混沌映射初始化种群,提高初始种群的多样性,增强算法的全局寻优能力;其次,将粒子群算法中各个粒子的速度策略引入发现者位置更新公式中,提升算法寻优能力;最后,使用高斯变异策略对最优麻雀位置进行扰动,增强了跳出局部最优的能力。将所提算法应用于9个不同特征的基准函数进行实验,结果表明,HSSSA算法有着良好的寻优精度和收敛速度。在GPS/BDS实测数据的3000个历元的解算中,相比传统LAMBDA算法和SSA算法,HSSSA算法有着更高的解算成功率,可达99.2%。

Aiming at the problems of weak global search ability and easy falling into local optimum in the process of calculating differential global positioning system(DGPS)integer ambiguity of Sparrow Search Algorithm(SSA),a hybrid strategy sparrow search algorithm(HSSSA)is proposed.Firstly,by introducing the Circle chaotic map to initialize the population,the diversity of the initial population is improved,and the global optimization ability of the algorithm is enhanced.Secondly,the speed strategy of each particle in the particle swarm algorithm is introduced into the finder position update formula to improve the optimization ability of the algorithm.Finally,the optimal sparrow position is perturbed by using a Gaussian mutation strategy,which enhances the ability to jump out of the local optimum.The proposed algorithm is applied to 9 benchmark functions with different characteristics for experiments.The results show that the HSSSA algorithm has good optimization accuracy and convergence speed.In the calculation of 3000 epochs of measured GPS/BDS data,compared with the traditional LAMBDA algorithm and the SSA algorithm,the HSSSA algorithm has a higher solution success rate,up to 99.2%.