Weighted Measurement Fusion Unscented Kalman Filter Using Gauss-Hermite Approximation for Nonlinear Systems
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摘要: 对非线性多传感器系统，基于Gauss-Hermite逼近方法和加权最小二乘法，提出了一种具有普适性的非线性加权观测融合算法.该算法可将一个高维观测压缩为一个低维观测.在此基础上，结合无迹Kalman滤波器（Unscented Kalman filter，UKF），提出了非线性加权观测融合无迹Kalman滤波器（WMF（Weighted measurement fusion）-UKF）.与集中式融合UKF（CMF（Centralized measurement fusion）-UKF）相比，该算法计算负担小且具有逼近的估计精度.特别是在传感器数量较大时，该算法在计算量上的优势更加明显.仿真例子验证了算法的有效性.
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关键词:
- 非线性系统 /
- 加权观测融合 /
- Gauss-Hermite逼近 /
- 无迹Kalman滤波器
Abstract: A universal nonlinear weighted measurement fusion algorithm is proposed using the Gauss-Hermite approximation method and the weighted least squares method for multi-sensor nonlinear systems. A high-dimensional measurement is compressed to a low dimensional measurement by this algorithm. Combined with the unscented Kalman filter (UKF), a nonlinear weighted measurement fusion UKF (WMF-UKF) is proposed based on the algorithm. Compared with the centralized measurement fusion UKF (CMF-UKF), the proposed algorithm has a reduced computational cost and approximate estimation accuracy. Its advantage in computation is obvious when the number of sensors is large. A simulation example illustrates the effectiveness of the algorithm.1) 本文责任编委 李鸿一 -
图 2 局部UKF, WMF-UKF以及CMF-UKF的AMSE曲线
Fig. 2 AMSE curves of local UKF, WMF-UKF and CMF-UKF
图 3 加权系数矩阵$\overline{M}$和$\overline{H}^{(\rm{I})}$的计算
Fig. 3 Calculation of the weighted matrices $\overline{M}$ and $\overline{H}^{(\rm{I})}$
图 4 真实轨迹和WMF-UKF, 8-CMF-UKF和5-CMF-UKF的估计曲线
Fig. 4 True and estimated tracks using WMF-UKF, 8-CMF-UKF and 5-CMF-UKF
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