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容积法则辅助的交互式多模型滤波算法

DOI: 10.13195/j.kzyjc.2013.0618, PP. 1719-1723

Keywords: 交互式多模型滤波,容积卡尔曼滤波,容积法则

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Abstract:

交互式多模型滤波(IMM)的交互环节使得系统状态量不再服从单纯的高斯分布,用现有方法对其概率分布的估计存在较大的误差.对此,考虑到模型的混合概率是时变的,IMM的交互过程可以用非线性方程来描述,因而采用容积卡尔曼滤波(CKF)中的容积法则对高斯随机变量经非线性函数传播后的概率分布进行估计,并从理论上证明了容积法则的近似精度.仿真实验表明,由于提高了对交互后随机变量概率分布的估计精度,所提出的方法能够有效改善IMM在量测噪声较大时的滤波效果.

References

[1]  石勇, 韩崇昭. 自适应UKF 算法在目标跟踪中的应用[J]. 自动化学报, 2011, 37(6): 754-759.
[2]  (Shi Y, Han C Z. Adaptive UKF method with applications to target tracking[J]. Acta Automatica Sinica, 2011, 37(6): 754-759.)?
[3]  Li X R, Jilkov V P. Survey of maneuvering target tracking, Part V: Multiple-model methods[J]. IEEE Trans on Aerospace and Electronic Systems, 2005, 41(4): 1255-1321.
[4]  Blom H A P, Bar-Shalom Y. The interacting multiple model algorithm for systems with Markovian switching coefficients[J]. IEEE Trans on Automatic Control, 1988, 33(8): 780-783.
[5]  盛琥, 杨景曙, 曾芳玲, 等. 引入输入估计的交互式多模型跟踪算法[J]. 电子学报, 2009, 37(12): 2810-2814.
[6]  (Sheng H, Yang J S, Zeng F L, et al. Interacting multiple model tracking algorithm with modified input estimation[J]. Acta Electronica Sinica, 2009, 37(12): 2810-2814.)
[7]  Jian L, Li X R, Chundi M. Second order Markov chain based multi-model algorithm for maneuvering target tracking[J]. IEEE Trans on Aerospace and Electronic Systems, 2013, 49(1): 3-19.
[8]  Cui N Z, Hong L, Jeffery R L. A comparison of nonlinear filtering approaches with an application to ground target tracking[J]. Signal Processing, 2005, 8(8): 1469-1492.
[9]  Li W L, Jia Y M. Gaussian mixture PHD filter for jump Markov models based on best-fitting Gaussian approximation[J]. Signal Processing, 2011, 91(4): 1036-1042.
[10]  Lainiotis D G, Sims F L. Performance measure for adaptive Kalman estimators[J]. IEEE Trans on Automatic Control, 1970, 15(2): 249-250.
[11]  Kirubarajan T, Bar-Shalom Y. Kalman filter versus IMM estimator: When do we need the latter[J]. IEEE Trans on Aerospace and Electronic Systems, 2003, 39(4): 1452-1456.
[12]  Williams J L. Gaussian mixture reduction for tracking multiple maneuvering targets in clutter[D]. Ohio: Department of the Air Force, Air University, 2003: 31-64.
[13]  侯代文, 殷福亮. 基于粒子滤波的交互式多模型说话人跟踪方法[J]. 电子学报, 2010, 38(4): 835-841.
[14]  (Hou D W, Yin F L. An IMM particle filtering method for speaker tracking[J]. Acta Electronica Sinica, 2010, 38(4): 835-841.)
[15]  Ienkaran A, Simon H. Cubature Kalman filters[J]. IEEE Trans on Automatic Control, 2009, 54(6): 1254-1269.
[16]  Fredrik G, Gustaf H. Some relations between extended and unscented Kalman filters[J]. IEEE Trans on Signal Processing, 2012, 60(2): 545-555.
[17]  Ienkaran A, Simon H, Thomas R H. Cubature Kalman filtering for continuous-discrete systems: Theory and simulations[J]. IEEE Trans on Signal Processing, 2010, 58(10): 4977-4993.
[18]  孙枫, 唐李军. Cubature 卡尔曼滤波器-卡尔曼滤波算法[J]. 控制与决策, 2012, 27(10): 1561-1565.
[19]  (Sun F, Tang L J. Cubature Kalman filter-Kalman filter algorithm[J]. Control and Decision, 2012, 27(10): 1561-1565.)
[20]  孙枫, 唐李军. Cubature 卡尔曼滤波器与Unscented 卡尔曼滤波估计精度比较[J]. 控制与决策, 2013, 28(2): 303-312.
[21]  (Sun F, Tang L J. Estimation precision comparison of Cubature Kalman filter and Unscented Kalman filter[J]. Control and Decision, 2013, 28(2): 303-312.)

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