摘要
在单轴旋转惯导系统中,轴向陀螺漂移是影响系统导航精度的重要因素。为了提高惯导系统的导航精度,采用混沌粒子群算法(chaos particle swarm optimization,CPSO)优化的最小二乘支持向量机(least squaressupport vector machine,LSSVM)对轴向激光陀螺漂移进行辨识。利用初始对准12h内系统纬度误差和温度变化量作为LSSVM模型的训练数据,利用CPSO对LSSVM进行参数优化,利用优化后的LSSVM模型对轴向陀螺漂移进行辨识,轴向陀螺漂移辨识精度优于0.000 2(°)/h,系统定位误差优于1nm/72h。试验结果表明,CPSO是选取LSSVM参数的有效方法,该方法能够有效地辨识轴向陀螺漂移,具有很高的辨识精度,具有很高的实际应用价值。
In order to improve the performance of the single-axis rotation inertial navigation system, a least squares support vector machine （LSSVM） model optimized by the chaos particle swarm optimization （CPSO） to identify the axial drift of ring laser gyroscope （RLG） is proposed. Latitude and temperature variation during the iden- tification stage are adopted as inputs of LSSVM. The CPSO is used to optimize the parameters of the LSSVM. The proposed CPSO-LSSVM can reach an identification accuracy of 0. 000 2 （°）/h for Z-axis RLG drift and radial po- sition error of the system is less than 1 nm/72 h. The navigational results show that the proposed method is an effective approach for LSSVM parameters and the identification model has a better identification precision. The proposed method can be widely used in engineering practice
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2013年第5期1049-1053,共5页
Systems Engineering and Electronics
基金
"十二五"国防装备预研基金资助课题
关键词
激光陀螺
惯导系统
单轴旋转
陀螺漂移
最小二乘支持向量机
混沌粒子群算法
ring laser gyroscope （RLG）
inertial navigation system
single-axis rotation
gyro drift
leastsquares support vector machine （LSSVM）
chaos particle swarm optimization （CPSO）
