Han Songlai, Xie Jingyi, Dong Jing
Research Institute of Aerospace Technology, Central South University, Changsha 410083, China.
Micromachines (Basel). 2025 Apr 29;16(5):524. doi: 10.3390/mi16050524.
The startup drift phenomenon that exists in MEMS INSs increases the navigation error, prolonging the start-up time. Aiming to resolve this problem, a startup drift compensation method based on a PSO-GRNN model is proposed in this paper. We adopted a correlation analysis to determine the input parameters of the PSO-GRNN model that mainly affect startup drift. In the process of training this model, we used the PSO algorithm to optimize the spread parameter of the PSO-GRNN model. The information transmission function between particle swarms was used to find the best spread parameter by iterative optimization, the particle's position was mapped to the GRNN model, and the GRNN model was constructed with the optimal position of the swarm as the spread parameter. This method can effectively compensate for startup drift and improve navigation accuracy. Startup drift compensation experiments were carried out at different ambient temperatures. Compared with the MEMS INS data without compensation, the standard deviation of the MEMS INS data with the proposed method decreased by more than 80.6%, and the peak-to-peak value of the MEMS INS data decreased by over 72.7%. Compared with the traditional method, the standard deviation of the MEMS INS data compensated via this method decreased by 54.5% on average, and the peak-to-peak value decreased by 42.8% on average. Meanwhile, the performance of this method was verified by navigation experiments. With the proposed method, the speed error improved by over 36.4%, and the position error improved by over 41.1%. The above experiments verified that the method of this paper significantly improved navigation performance.
微机电系统惯性导航系统（MEMS INSs）中存在的启动漂移现象会增加导航误差，延长启动时间。针对这一问题，本文提出了一种基于粒子群优化广义回归神经网络（PSO-GRNN）模型的启动漂移补偿方法。我们采用相关性分析来确定主要影响启动漂移的PSO-GRNN模型的输入参数。在训练该模型的过程中，我们使用粒子群算法（PSO）来优化PSO-GRNN模型的扩展参数。利用粒子群之间的信息传递函数，通过迭代优化找到最佳扩展参数，将粒子的位置映射到GRNN模型，并以群体的最优位置作为扩展参数构建GRNN模型。该方法能够有效补偿启动漂移，提高导航精度。在不同环境温度下进行了启动漂移补偿实验。与未补偿的MEMS INS数据相比，采用本文方法的MEMS INS数据的标准差降低了80.6%以上，峰峰值降低了72.7%以上。与传统方法相比，通过该方法补偿的MEMS INS数据的标准差平均降低了54.5%，峰峰值平均降低了42.8%。同时，通过导航实验验证了该方法的性能。采用本文方法，速度误差提高了36.4%以上，位置误差提高了41.1%以上。上述实验验证了本文方法显著提高了导航性能。
