作者：李宇¹, 周洁¹, 申强², 高嵩¹

作者单位：1. 西安工业大学电子信息工程学院，陕西 西安 710021;
2. 西北工业大学 空天微纳系统教育部重点实验室，陕西 西安 710072

关键词：多传感器融合;未知输入;两级信息滤波;偏差估计

摘要：

针对目标跟踪过程中受未知输入影响的多传感器网络，提出一种局部单传感器抗干扰信息滤波算法并根据此算法实现分布式一致性多传感器融合滤波估计实现目标的精确跟踪。首先，建立包含未知输入的系统模型；其次，消除未知输入影响并设计局部单传感器两级信息滤波算法实现状态和广义偏差的同时估计；最后，根据提出的单传感器两级信息滤波算法进行分布式加权数据融合。仿真结果表明，该方法及其融合算法的系统偏差、状态估计误差和均方根误差均明显降低，目标跟踪精度有所提高，并且具有较低的运算量和较高的一致性。

Distributed two-stage information filtering and bias estimation for heterogeneous multi-sensor under unknown input interference
LI Yu¹, ZHOU Jie¹, SHEN Qiang², GAO Song¹
1. School of Electronic Information Engineering, Xi’an Technological University, Xi’an 710021, China;
2. MOE Key Laboratory of Micro/Nano Systems for Aerospace, Northwestern Polytechnical University, Xi’an 710072, China
Abstract: This paper address the problem of the joint local single-sensor filtering algorithm and its centralized multi-sensor fusion filtering estimation under a common unknown input in the case of bias evolution in the linear discrete random time-varying systems. First, a system model containing unknown input is established. Second, the effects of unknown inputs are eliminated, to achieve simultaneous estimation of state and bias, a local single-sensor two-stage filtering algorithm is designed. Finally, based on the single-sensor two-stage filtering algorithm, a centralized data fusion is designed. Simulation results show that the system deviation, state estimation error and root mean square error of this method and its fusion algorithm are significantly reduced. The improved accuracy proves that this method is very effective.
Keywords: multi-sensor fusion;unknown input;two-stage filter;bias estimation
2021, 47(2):125-132  收稿日期: 2020-11-06;收到修改稿日期: 2020-12-31
基金项目: 陕西省重点研发计划项目(2019GY-066)；陕西省教育厅专项科研计划项目(19JK0407)；航空基金项目(201958053002)；陕西科技新星支持项目(2020KJXX-072)
作者简介: 李宇(1997-),女,陕西镇安县人,硕士研究生,专业方向为模式识别与智能系统
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