GPS/INS Integrated Navigation Based on Grasshopper Optimization Algorithm. Issue 24 (2019)

• Record Type: Journal Article
• Authors: Meng, Hao; Han, Yu; Xun, Yuting; Chen, Jian; Du, Nannan; Cao, Yi; Wang, Guangqi; Yi, Xiangwei; Zheng, Yongjun
• Is Part Of: IFAC-PapersOnLine. Volume 52:Issue 24(2019)
• Page Start: 29
• Page End: 34
• Publication Date: 2019
• Subjects: grasshopper optimization algorithm -- GNSS/INS integrated navigation -- Kalman filter -- longitudinal -- latitude errors -- simulation experiment; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2019.12.374
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In GPS/INS integrated navigation, Kalman filter is usually used for data fusion between GPS and INS. The filtering algorithm of integrated navigation is related to whether the advantages of each sensor in the integrated navigation can be utilized, the navigation accuracy is improved, the reliable working time of the navigation system is improved and the navigation requirement are met. In this paper, we proposed an approach based on the Grasshopper optimization algorithm (GOA), which is a recent algorithm inspired by the biological behavior shown in swarm of grasshoppers. The goal of the proposed approach is to optimize the parameters of the Kalman filter. The Kalman filter method is improved by grasshopper optimization algorithm, which improves the accuracy of integrated navigation and reduces the errors caused by system noise and measurement noise. For verification, the proposed approach is compared with particle swarm optimization. The simulation experiment results show that the proposed approach has a better effect.