摘要
在GPS和捷联式惯性导航系统(strapdown inertial navigation system,SINS)的超紧组合结构中,惯性测量单元(inertial measurement unit,IMU)和星历信息等估计得到的载波多普勒辅助值是影响跟踪环路性能的主要因素,因此提出一种应用自回归滑动平均(auto-regressive moving average,ARMA)模型对估计过程中存在的随机多普勒误差建模,精确估计载波多普勒方法。通过判定随机多普勒误差序列的平稳性,采用时间序列分析的方法建立数学模型;根据随机误差序列的自相关函数和偏相关函数特性,确定模型类型和阶数;利用Yule-Walker方法估计模型参数,得到符合该随机序列的数学模型。仿真结果表明,该模型具有很好的适用性,能准确拟合随机多普勒误差,为超紧组合系统中的辅助环路提供精确的载波估计。
In the ultra-tight coupled structure of GPS and strapdown inertial navigation system（SINS）,the value of carrier Doppler estimated by inertial measurement unit（IMU）,satellite ephemeris and other information is the main factor to affect the performance of carrier tracking loop.So an accurate carrier Doppler estimation method using auto regressive moving average（ARMA）model is proposed.The time series analysis method is applied to establish a mathematical model by means of determining the stationariness of stochastic Doppler error sequence.The model type and order number are selected by studying the autocorrelation function and the partial correlation function of the stochastic error sequence.The Yule-Walker method is applied to estimate the model parameters and an appropriate model is obtained.Simulation results indicate that the model is suitable for application,which could fit the stochastic Doppler error accurately and provide precise estimates for aided carrier tracking loop in ultra-tight system.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2011年第5期1085-1088,共4页
Systems Engineering and Electronics
基金
国家自然科学基金(60974104)资助课题
关键词
超紧组合
载波多普勒
自回归滑动平均
卡尔曼滤波器
ultra-tight couple
carrier Doppler
auto regressive moving average（ARMA）
Kalman filter
