中心差分卡尔曼滤波是目标跟踪领域中常用的非线性滤波方法之一，但在单站的纯方位目标跟踪中，有时受观测站的运动轨迹、观测噪声等影响，中心差分卡尔曼滤波会出现滤波不稳定甚至发散的情况。针对这一问题，本文提出一种基于奇异值分解平方根的中心差分卡尔曼滤波改进方法。通过采用QR分解和奇异值分解2种不同的方式计算协方差的平方根，代替协方差矩阵参与运算，增强算法的稳定性。通过3种不同情形下的仿真结果均表明，所提方法与常规的中心差分卡尔曼滤波和经典的平方根无迹卡尔曼滤波方法相比，具有最低的均方根误差。
Central differential Kalman filter is one of the most commonly used nonlinear filtering methods in the field of target tracking. However, in the bearings only target tracking by single observer, the central differential Kalman filter is unstable or even divergent due to the influence of the motion trajectory of the observer and observation noise. To solve this problem, an improved central difference Kalman filter based on SVD square root is proposed. The QR decomposition and singular value decomposition are used to calculate the square root of covariance instead of covariance matrix to enhance the stability of the algorithm. The simulation results of three different cases show that the root mean square error of the proposed method is the lowest compared with the conventional central difference Kalman filter and the classical square root unscented Kalman filter.
2021,43(1): 154-160 收稿日期：2020-09-03
DOI：10.3404/j.issn.1672-7649.2021.01.029
分类号：TN958
作者简介：郑艺(1992-),女,博士研究生,研究方向为水下目标跟踪定位
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