Statically Fused Converted Position and Doppler Measurement Kalman Filters
Statically Fused Converted Position and Doppler Measurement Kalman Filters
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静态融合转换位置和多普勒测量卡尔曼滤波器
DOI：
10.1109/taes.2013.120256
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发表时间：
2014-05
期刊：
影响因子：
--
通讯作者：
Taifan Quan
中科院分区：
文献类型：
--
作者：
Gongjian Zhou;Michel Pelletier;Thiagalingam Kirubarajan;Taifan Quan
本文提出的工作，利用多普勒（距离变化率）测量跟踪系统的两个贡献。首先，提出了一种新的线性滤波器，转换多普勒测量卡尔曼滤波器（CDMKF），用于从转换多普勒测量（即，距离测量和多普勒测量的乘积）。赝态是由转换后的多普勒及其导数构成的。针对常见的目标运动模型，导出了伪态的线性演化方程。本文的第二个贡献是沿着将CDMKF与仅使用位置测量的转换位置测量卡尔曼滤波器（CPMKF）相结合，建立了一种新的滤波结构--静态融合转换测量卡尔曼滤波器（SF-CMKF）。CPMKF和CDMKF得到的状态由静态最小均方误差（MMSE）估计器组合，其中伪状态和笛卡尔状态之间的非线性和相关性被同时处理，以产生最终的状态估计。将动态非线性估计问题转化为动态线性估计和静态非线性融合问题。通过线性CDMKF结合多普勒测量可以提高估计精度，同时通过处理滤波递归之外的非线性可以提高滤波稳定性。Monte Carlo仿真和与后验Cramer-Rao界的比较证明了CDMKF和SF-CMKF的有效性。
The work presented in this paper makes two contributions for exploiting Doppler (range rate) measurements in tracking systems. First, a new linear filter, the converted Doppler measurement Kalman filter (CDMKF), is presented to extract nonlinear pseudostates from converted Doppler measurements (i.e., the product of the range measurements and Doppler measurements). The pseudostates are constructed from the converted Doppler and its derivatives. The linearly evolving equations of the pseudostates are derived for common target motion models. The second contribution of this paper is using the CDMKF along with the converted position measurement Kalman filter (CPMKF), in which only the position measurements are used, to establish a new filtering structure, statically fused converted measurement Kalman filters (SF-CMKF). The resulting states of CPMKF and CDMKF are combined by a static minimum mean squared error (MMSE) estimator, where the nonlinearity and correlation between the pseudostates and the Cartesian states are handled simultaneously, to yield the final state estimates. The dynamic nonlinear estimation problem is converted into dynamic linear estimation followed by static nonlinear fusion. The estimation accuracy can be enhanced by incorporating the Doppler measurements via the linear CDMKF, while the filtering stability can be improved by dealing with nonlinearity outside the filtering recursions. Monte Carlo simulations and comparison with the posterior Cramer-Rao bound demonstrate the effectiveness of the CDMKF and SF-CMKF.
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发表时间：
2003-07
期刊：
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作者：
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