摘要
为提高标定捷联惯组动态误差系数的速度,提出在惯性导航系统中建立动力学误差模型,利用卡尔曼滤波反映误差项的观测度,确定最优算法的目标函数。针对减空间搜索算法的罚函数采用遗传算法进行分析改进,在末端时,既摆脱了局部伪最优解对结果的干扰,又在接近最优解时提高辨识速度。与共轭梯度法的求解时长进行比较,验证了改进减空间搜索算法对动态系数标识的高效性,有效解决捷联惯组动态误差无法快速补偿的问题。
In order to improve the dynamic error coefficient speed of the calibration strapdown inertial group, a dynamic error model is established in the inertial navigation system. The Kalman filter is used to reflect the observation degree of the error term to determine the objective function of the optimal algorithm. The penalty function for the reduced space search algorithm is improved by genetic algorithm. At the end, it not only gets rid of the interference of the local pseudo-optimal solution, but also improves the recognition speed when it is close to the optimal solution. Compared with the solution length of the conjugate gradient method, the efficiency of the improved reduced space search algorithm for dynamic coefficient identification is verified, and the problem that the dynamic error of the strapdown inertial group cannot be quickly compensated is effectively solved.
作者
闫宏雁
张宇
朱伟华
史明
张业鑫
Yan Hongyan;Zhang Yu;Zhu Weihua;Shi Ming;Zhang Yexin(Shanghai Institute of Mechanical and Electrical Engineering,Shanghai 201100,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2019年第11期2485-2491,共7页
Journal of System Simulation
关键词
捷联式惯性导航系统
共轭梯度算法
减空间搜索法
动态误差标定
stapdown inertial navigation systems
conjugate gradient algorithm
subtractive space search algorithm
dynamic error calibration
