Kalman Filter Based on Measurement Lifting Strategy

HU Zhen-tao; HU Yu-mei; LIU Xian-xing

doi:10.3969/j.issn.0372-2112.2016.05.019

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Kalman Filter Based on Measurement Lifting Strategy

更新时间：2025-07-16

- Kalman Filter Based on Measurement Lifting Strategy
- Acta Electronica Sinica Vol. 44, Issue 5, Pages: 1149-1155(2016)
- 作者机构：
  
  河南大学图像处理与模式识别研究所,河南,开封,475004
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61300214);China Postdoctoral Science Foundation (No.2014M551999);Science and Technology Innovation Team Support Plan of Colleges and Universities in Henan Province (No.13IRTSTHN021);Henan Research Program of Basic and Frontier Technology (No.132300410148);Postdoctoral Science Foundation of Henan Province (No.2013029);Funding Project for Young Backbone Teachers of Universities in Henan Province (No.2013GGJS-026);Excellent Youth Cultivation Fund of Henan University (No.0000A40366)
- DOI：10.3969/j.issn.0372-2112.2016.05.019
  CLC： TP391
- Published Online：25 May 2016，
  
  Published：2016
- 稿件说明：
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HU Zhen-tao, HU Yu-mei, LIU Xian-xing. Kalman Filter Based on Measurement Lifting Strategy[J]. Acta Electronica Sinica, 2016, 44(5): 1149-1155.
DOI：

HU Zhen-tao, HU Yu-mei, LIU Xian-xing. Kalman Filter Based on Measurement Lifting Strategy[J]. Acta Electronica Sinica, 2016, 44(5): 1149-1155. DOI： 10.3969/j.issn.0372-2112.2016.05.019.

摘要

滤波器设计是系统辨识和状态估计的重要基础.卡尔曼滤波通过状态预测和量测更新的实现框架

在最小方差准则下实现对目标状态的最优估计

但在单传感器量测环境中其滤波精度易受量测噪声随机性的影响.本文提出一种基于量测提升策略的卡尔曼滤波算法实现框架

新方法依据当前时刻量测和量测噪声先验统计信息构建虚拟量测

并通过对虚拟量测采样以及融合提升系统量测信息可靠性

进而改善状态估计精度.同时

针对算法在工程应用中实时性、准确性以及鲁棒性等需求

设计了分布式加权融合和集中式一致性融合的两种实现结构.理论分析和仿真实验结果验证了算法的可行性和有效性.

Abstract

Filter design is the signification foundation for system identification and state estimation.Based on the realization construction of state prediction and measurement update

Kalman filter can obtain the optimal estimation of state estimated under the linear minimum variance criterion

but the filtering precision is vulnerable to the random characteristics in single sensor condition.A novel realization structure of Kalman filter based on measurement lifting strategy is proposed in the paper.At first

virtual measurement is constructed on the basis of latest measurement and the prior statistical information of measurement noise modeling.Then

virtual measurements are reasonably sampled and fusion to modify the measurement reliability

and the estimation precision is improved.In addition

aiming to the algorithm requirements including real-time

precise and robustness in engineering application

the distributed weight fusion structure and the centralized consistency fusion are designed respectively.Finally

the theoretical analysis and experimental results show the feasibility and efficiency of algorithm proposed.

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