基于最小二乘和EKF的伪卫星定位算法  

Pseudolite positioning algorithm based on least squares and EKF

作　　者：付广仁[1] 曹正文[1,2] 米林山[3] 孙德禄[1] 张应贤[1] 

机构地区：[1]西北大学信息科学与技术学院,西安710127 [2]西北工业大学电子信息学院,西安710072 [3]中国人民解放军96325部队

出　　处：《计算机工程与应用》2012年第34期148-151,共4页Computer Engineering and Applications

基　　金：陕西省教育厅自然科学专项基金(No.12JK0497)

摘　　要：空基伪卫星由于自身机动性以及受到诸如气流、压力、温度等外界因素的影响使得其位置存在着偏移。因此,精确确定空基伪卫星的位置是其增强现有导航系统或独立组网进行导航定位的前提。针对扩展Kalman滤波对初值的要求和最小二乘法估计性好的特点,提出了一种混合算法,该算法用逆定位原理建立伪距观测方程组并采用最小二乘法解算出初值,运用扩展Kalman滤波进行定位。仿真表明,混合算法优于最小二乘法,定位精度得到了提高。The location of space-based pseudolite exists offset because of its own mobility and influence of external factors, such as flow, pressure, temperature, etc. Therefore, to accurately determine the location of space-based pseudolite is a premise for enhancement of the existing navigation system or formation of an independent navigation and positioning network. Aiming to Extended-Kalman filter requiring the initial value and the least squares method good at estimation, this paper puts forward a blending algorithm. In this blending algorithm, the algorithm uses the reverse positioning theory to establish pseudorange observation equations, then the least squares method is used to calculate the initial value, and the space-based pseudolite is positioned by using Extended-Kalman filter. The simulation results show that the blending algorithm is better than the least squares method, and the positioning accuracy has been improved.

关 键 词：伪卫星 最小二乘法 扩展卡尔曼滤波 逆定位 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]