一种GPS窄带干扰自适应卡尔曼滤波算法

An Adaptive Kalman Filtering Algorithm for Narrowband Interference on GPS

针对GPS信号中存在的窄带干扰,提出了一种基于自回归(AR)模型的自适应卡尔曼滤波算法,并分析了其用于非平稳滤波的可行性.将自回归参数作为状态向量,结合样本数据,建立系统的状态空间模型,并对自回归模型进行定阶及参数估计,同时利用所得到的自回归模型对GPS数据进行处理,并与递推最小二乘法(RLS)和最小均方(LMS)算法进行对比,证明了其滤波误差较低、收敛速度快.

For the narrowband interference on GPS, an auto-regressive （AR） model based adaptive Kalman filtering al- gorithm is presented. The feasibility of the algorithm for nonstationary signal filtering is analyzed. With the swatch data, the state space model of system is built by taking the auto-regressive parameter as state vector. The parameter and the rank of auto-regressive model are estimated. The AR model based algorithm is used to process the GPS data. Compared with the recursive least square （RLS） and least mean square （LMS） algorithms, the adaptive filtering algorithm has lower filtering error and higher convergence speed.