基于MVEKF算法的机载单站无源定位

介绍了基于相位差变化率的机载单站无源定位原理,并引入一种新的MVEKF(修正协方差扩展卡尔曼滤波)算法。文中详细介绍了MVEKF算法的基本原理,并将其应用于相位差变化率体制下的机载单站无源定位中,给出了MVEKF滤波方程;通过仿真将其与常用的非线性滤波算法EKF(推广卡尔曼滤波)和MGEKF(修正增益扩展卡尔曼滤波)进行了比较。结果表明,与EKF算法相比,MVEKF不易受初始状态估计的影响,且收敛速度更快,滤波效果更具稳定性;此外,无须寻找观测方程修正函数,MVEKF算法就可达到与MGEKF算法相当的滤波效果,因而为机载无源定位向快速、高精度方向的发展提供了一定的理论依据。

Algorithm for Passive Localization Based on MVEKF (Modified coVariance Extended Kalman Filter)

MVEKF algorithm utilizes phase-difference rate of change to implement passive localization.In the full paper,we explain our MVEKF algorithm in detail;in this abstract,we just add some pertinent remarks to listing the three topics of explanation:(1) the passive localization principles based on phase-difference rate of change,(2) the details of MVEKF algorithm,and(3) the application of MVEKF to passive localization;in topic 1,eqs.(1) and(2) in the full paper are taken from the open literature;in topic 2,eqs.(3) through(7) in the full paper are also taken from the open literature;in topic 3,we select suitable state variables for a fixed target on ground and derive state equation,i.e.,eq.(10) in the full paper;then,also in topic 3,using the passive localization principles based on phase-difference rate of change,we derive the system of filter equations for MVEKF,i.e.,eqs.(11) through(18) in the full paper.The simulation results,given in Figs.1 and 2 in the full paper,show that,compared with EKF algorithm,MVEKF algorithm converges faster,possesses much better filtering stability,and,moreover,it is not easily influenced by the selection of initial state.The above-mentioned results also show that almost the same filtering effect as MGEKF can be achieved by MVEKF without the necessity of searching for modification function for measurement equation.