| 引用本文: | 汪语哲,史小平,张汝波,毛琳,李佳乐.一种分块差分滤波器及其在仅有角度跟踪中的应用[J].哈尔滨工业大学学报,2018,50(9):130.DOI:10.11918/j.issn.0367-6234.201708001 |
| WANG Yuzhe,SHI Xiaoping,ZHANG Rubo,MAO Lin,LI Jiale.A divided differential filter and its application in bearings-only tracking[J].Journal of Harbin Institute of Technology,2018,50(9):130.DOI:10.11918/j.issn.0367-6234.201708001 |
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| 一种分块差分滤波器及其在仅有角度跟踪中的应用 |
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汪语哲1,3,4,5,史小平2,张汝波1,3,毛琳1,3,李佳乐1,3
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( 1.大连民族大学 机电工程学院, 辽宁 大连 116600;2.哈尔滨工业大学 控制与仿真中心,哈尔滨 150001; 3.智能感知与先进控制国家民委重点实验室(大连民族大学),辽宁 大连 116600;4.大连理工大学 土木工程博士后流动站,辽宁 大连 116024; 5.大连葆光节能空调设备厂, 辽宁 大连 116000)
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| 摘要: |
| 为解决仅有角度跟踪时，目标估计受限于较大的初始估计误差和噪声统计特性未知的问题，提出了一种带有噪声智能统计功能的改进型分块差分滤波器.通过统计线性化方法得到了一种S-H智能噪声统计估值器，并用其优化传统分块噪声滤波器的测量更新步骤，实现了对未知过程和测量噪声的智能统计处理，通过迭代更新进一步提高了滤波器对于复杂非线性函数的适应能力.与目前几类主流的自适应滤波器性能相比，结果表明:对于具有线性系统模型和非线性测量模型的典型被动跟踪估计问题，针对较大的初始状态估计误差，所给出的滤波器能更好地完成系统噪声和测量噪声部分参数统计特性未知情况下的非线性估计任务，在保证计算量适中的同时有效地提高跟踪制导精度.
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| 关键词: 分块差分 被动跟踪 S-H智能噪声统计 滤波器 仅有角度跟踪 |
| DOI：10.11918/j.issn.0367-6234.201708001 |
| 分类号:TN911.23 |
| 文献标识码:A |
| 基金项目:国家自然科学基金 (61673084)；辽宁省自然科学基金(20170540024)；大连市青年科技之星项目(2017RQ022)；金普新区软科学项目(2017) |
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| A divided differential filter and its application in bearings-only tracking |
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WANG Yuzhe1,3,4,5,SHI Xiaoping2,ZHANG Rubo1,3,MAO Lin1,3,LI Jiale1,3
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(1.College of Electromechanical Engineering, Dalian Minzu University, Dalian 116600, Liaoning, China; 2.Control and Simulation Centre, Harbin Institute of Technology, Harbin 150001, China; 3.Key Lab of Intelligent Perception and Advanced Control(Dalian Minzu University),Dalian 116600, Liaoning, China; 4.Center for Post-doctoral Studies of Civil Engineering, Dalian Institute of Technology, Dalian 116024, Liaoning, China; 5.Dalian Baoguang Energy Saving Air Conditioning Corporation, Dalian 116600, Liaoning, China)
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| Abstract: |
| To solve the problems of estimation accuracy restricted by large initial estimation error and unknown noise statistics in bearings-only tracking, a divided differential filter with intelligent statistical noise estimator was proposed. An S-H intelligent noise statistical estimator was proposed according to statistical linear regression theories, and was used for optimizing measurement update step of traditional divided differential filter, thus unknown state and noise measurement were intelligently and statistically calculated. The ability of the filter to adapt to complex nonlinear functions was further improved by iteration updating. Results showed that for typical passive tracking problem in linear state function and nonlinear measurement function with relative large initial estimation errors, the proposed filter provided better performance of nonlinear estimation task compared to several mainstream adaptive filters when the statistical characteristics of the system noise and measurement noise were unknown, and it effectively enhanced tracking and guidance accuracy and guaranteed moderate level of computation load at the same time.
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| Key words: divided differential passive tracking S-H intelligent noise estimator filter bearings-only tracking |
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