基于自适应EKF的相对导航算法研究

针对测距测角相对导航中测量噪声不可精确获知往往导致相对定位精度下降的问题,本文研究了基于自适应扩展卡尔曼滤波(EKF)的相对导航算法。利用泰勒级数展开对测量矩阵进行线性化处理,并利用自适应时变噪声估计方法对测量噪声方差阵进行动态估计,状态噪声方差阵通过惯导特性的先验值获得。仿真结果表明,基于自适应EKF的相对导航算法可获得高精度且连续平滑的相对定位信息,尤其在测量噪声发生变化时更是表现出良好的导航参数估计性能。

Study on Relative Navigation Algorithm Based on Adaptive Extended Kalman Filter

A relative navigation algorithm based on the adaptive extended Kalman filter is studied in this paper to solve problems of the decline of relative position precision, which is induced by the cause that the measurement noise cannot accurately know. Firstly, the Taylor series expansion is applied to transform the measurement matrix to the linearization model. Secondly, the covariance matrix of the measurement noise is estimated dynamically by using the estimation method of adaptive time-variant noise, and the covariance matrix of the state noise is got via the prior knowledge of inertial navigation. Finally, the simulation results indicate that the research method in this paper can provide continuous and smooth relative position information with highly precision, and the method still show the better navigation parameter estimation performance in the case of the varying measurement noise happens especially.