基于并行卡尔曼滤波的递推最小二乘测频算法  

A Least Squares Frequency Measurement Algorithm Based on the Parallel Kalman Filter

作　　者：孟真[1] 于进勇[1] 阎跃鹏[1] 

机构地区：[1]中国科学院微电子研究所,北京100029

出　　处：《微电子学与计算机》2011年第3期1-5,12,共6页Microelectronics & Computer

基　　金：国家高技术研究发展计划("八六三"计划)(2009AA12Z314)

摘　　要：提出了一种基于并行卡尔曼滤波的递推最小二乘算法.利用最小二乘算法对卡尔曼滤波的并行多路收敛输出值进行二次优化估计.将本算法应用于正弦波测频,与只采用卡尔曼滤波的算法相比,可使频率估计精度得到明显提高.仿真证明在信噪比0-100 dB区间内采用本算法为卡尔曼滤波算法的测频误差的1/10左右.Presents a Least Squares frequency measurement algorithm based on the parallel Kalman filter.The convergence values of the Kalman filter is in accordance to the input model of the Least squares algotithm,so Least squares algorithm can be used to optimization the output of the Kalman filter.The frequency estimation accuracy can be increased significantly by using this algorithm to only using Kalman filter.It is proved that the frequency estimation error can be reduced to about 1/10 when using the Least Squares frequency measurement algorithm based on the parallel Kalman filter instead of only using the Kalman filter.

关 键 词：卡尔曼滤波 最小二乘 高斯分布 测频 

分 类 号：TN92[电子电信—通信与信息系统]