自适应推广Kalman滤波应用于水下被动目标定位

摘要

针对在被动方式下进行水下目标定位容易出现滤波发散、收敛速度慢以及精度不高等问题，研究了一种修正极坐标系下的自适应推广卡尔曼滤波算法。它能够在线估计虚拟系统噪声的统计特性，从而消除了动态模型线性化误差带来的不良影响。仿真结果表明，该算法在收敛速度、估计精度以及稳定性方面都优于常规的卡尔曼滤波器。%Concerning the problem of instability, slow convergence and low accuracy of passive filter in underwater emitter location, an adaptive extended Kalman filter (AEKF) algorithm in modified polar coordinate is presented. Owing to estimate the statistics of virtual noise on-line, it overcomes the bad affect caused by linearization of nonlinear dynamic model. Simulation results show that the AEKF performs better in aspects of convergence, tracking accuracy and stability.