贝叶斯序贯重要性积分滤波器
Bayesian Sequential Importance Quadrature Filter
查看参考文献25篇
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文摘
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为解决非线性滤波中存在模型歧义和预测偏差情况下似然函数、目标重要性密度函数和实际目标分布不匹配的问题,提出了贝叶斯序贯重要性积分滤波器(Bayesian Sequential Importance Quadrature Filter,SIQF).为了消减贝叶斯推理中似然函数和目标分布之间的偏差,通过空时软约束定义最新观测的有界似然,截断观测噪声概率密度函数以近似可行域的修正先验.为了调制重要性函数和目标分布的匹配程度,并行对修正和原始先验下的状态进行Gauss-Hermite积分,引入最大相关信息熵构建覆盖多模分布的重要性函数,从而提升序贯重要性采样的多样性和预测协方差的容错性.实验结果表明:相比无迹粒子滤波估计一维单变量增长模型,SIQF算法在无需牺牲计算复杂度的情况下平均误差减小了63%;相比多模型Rao-blackwell粒子滤波器跟踪空域机动目标,SIQF算法的均方根误差减小了33%,所需计算量降低了一个数量级. |
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其他语种文摘
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To solve the mismatch problems between the measurement likelihood function, importance density function and the target true distribution for the nonlinear filtering in the presence of the model attribute ambiguity and prediction bias, we derive and present a Bayesian sequential importance quadrature filter(SIQF)algorithm. To reduce the deviation between the likelihood function and the target true distribution in the Bayesian reference, the bounded measurement likelihood of the latest measurement is defined via the soft spatiotemporal constraint, the modified prior of the feasible area is approximated by truncating the probability density function of the measurement noise. To modulate the matching degree between the importance function and the target distribution, the state under the modified and original priors is evaluated via Gauss- Hermite Kalman filter in parallel, the maximum correntropy criterion is introduced to construct the mixture importance function, both the diversity of sequential importance sample and the tolerance of prediction covariance can be thereby improved. The simulation results show that, compared with the unscented particle filter for the estimation of one-dimensional univariate nonstationary growth model, the average estimate error of the SIQF algorithm has decreased 63% without sacrificing computational complexity. Compared with the multi-model Rao-blackwell particle filter for the maneuvering target tracking in the airspace, the root mean square error of the SIQF algorithm has decreased 33%, and the computational load is reduced by an order of magnitude. |
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来源
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电子学报
,2022,50(4):823-831 【核心库】
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DOI
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10.12263/DZXB.20210716
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关键词
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非线性滤波
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贝叶斯序贯重要性积分
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软约束
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有界似然
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相关信息熵测度
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地址
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1.
中山大学航空航天学院, 广东, 广州, 510725
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中国科学院空间精密测量技术重点实验室, 中国科学院空间精密测量技术重点实验室, 陕西, 西安, 710119
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北京东方计量测试研究所, 北京, 100029
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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0372-2112 |
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学科
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电子技术、通信技术 |
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基金
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广东省基础与应用基础研究基金
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中山大学青年培育项目
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中国科学院空间精密测量重点实验室开放基金
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文献收藏号
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CSCD:7190609
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参考文献 共
25
共2页
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|
1.
Gelman A.
Bayesian Data Analysis,1995
|
CSCD被引
36
次
|
|
|
|
|
2.
De-Geeter J. A smoothly constrained Kalman filter.
IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(10):1171-1177
|
CSCD被引
9
次
|
|
|
|
|
3.
Julier S J. Unscented filtering and nonlinear estimation.
Proceedings of the IEEE,2004,92(3):401-422
|
CSCD被引
769
次
|
|
|
|
|
4.
Arasaratnam I. Discretetime nonlinear filtering algorithms using Gauss-hermite quadrature.
Proceedings of the IEEE,2007,95(5):953-977
|
CSCD被引
61
次
|
|
|
|
|
5.
Garcia-Fernandez A F. Truncated unscented Kalman filtering.
IEEE Transactions on Signal Processing,2012,60(7):3372-3386
|
CSCD被引
11
次
|
|
|
|
|
6.
Andrieu C. An introduction to MCMC for machine learning.
Machine Learning,2003,50(1/2):5-43
|
CSCD被引
80
次
|
|
|
|
|
7.
Bugallo M F. Adaptive importance sampling: The past, the present, and the future.
IEEE Signal Processing Magazine,2017,34(4):60-79
|
CSCD被引
5
次
|
|
|
|
|
8.
Doucet A. On sequential Monte Carlo sampling methods for Bayesian filtering.
Statistics and Computing,2000,10(3):197-208
|
CSCD被引
356
次
|
|
|
|
|
9.
Xie W X. An information measure for fuzzy sets.
IEEE Transactions on Systems, Man, and Cybernetics,1984,14(1):151-156
|
CSCD被引
6
次
|
|
|
|
|
10.
Simon D. Kalman filtering with state constraints: A survey of linear and nonlinear algorithms.
IET Control Theory & Applications,2010,4(8):1303-1318
|
CSCD被引
68
次
|
|
|
|
|
11.
Bar-Shalom Y.
Estimation with Applications to Tracking and Navigation,2001
|
CSCD被引
103
次
|
|
|
|
|
12.
Tronarp F. Iterative filtering and smoothing in nonlinear and non-Gaussian systems using conditional moments.
IEEE Signal Processing Letters,2018,25(3):408-412
|
CSCD被引
4
次
|
|
|
|
|
13.
Van-Der-Merwe R. The unscented particle filter.
Advances in Neural Information Processing Systems,2000,13:584-590
|
CSCD被引
1
次
|
|
|
|
|
14.
Elvira V. Importance Gaussian quadrature.
IEEE Transactions on Signal Processing,2021,69:474-488
|
CSCD被引
2
次
|
|
|
|
|
15.
Kirubarajan T. Kalman filter versus IMM estimator: When do we need the latter?.
IEEE Transactions on Aerospace and Electronic Systems,2003,39(4):1452-1457
|
CSCD被引
30
次
|
|
|
|
|
16.
Hostettler R. Rao-blackwellized Gaussian smoothing.
IEEE Transactions on Automatic Control,2019,64(1):305-312
|
CSCD被引
3
次
|
|
|
|
|
17.
王琪. 基于Rao-Blackwellised粒子滤波的相控阵导引头指向误差斜率在线估计.
电子与信息学报,2021,43:1-8
|
CSCD被引
1
次
|
|
|
|
|
18.
Zhang H W. Constrained unscented Kalman filtering for bearings-only maneuvering target tracking.
Chinese Journal of Electronics,2020,29(3):501-507
|
CSCD被引
1
次
|
|
|
|
|
19.
Liu C J. Particle filtering with soft state constraints for target tracking.
IEEE Transactions on Aerospace and Electronic Systems,2019,55(6):3492-3504
|
CSCD被引
4
次
|
|
|
|
|
20.
蒋林. 加载语义似然估计的粒子滤波重定位.
电子学报,2021,49(2):306-314
|
CSCD被引
6
次
|
|
|
|
|
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