引用本文: | 梁彦 ,潘泉 , 张洪才.常增益交互式多模型算法*[J].控制理论与应用,1999,16(5):659~663.[点击复制] |
Liang Yan , Pan Quan and Zhang Hongcai.Interacting Multiple Models Algorithm Based on α-β & α-β-γ Filters[J].Control Theory and Technology,1999,16(5):659~663.[点击复制] |
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常增益交互式多模型算法* |
Interacting Multiple Models Algorithm Based on α-β & α-β-γ Filters |
摘要点击 1208 全文点击 499 投稿时间:1998-03-25 修订日期:1998-11-25 |
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DOI编号 |
1999,16(5):659-663 |
中文关键词 自适应滤波 常增益滤波 目标跟踪 |
英文关键词 adaptive filtering α-βfilter α-β-γ filter tracking |
基金项目 |
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中文摘要 |
针对混合估计自适应滤波器的工程应用问题,首先证明了交互式多模型算法(IMM)在一定条件下,其模型输入交互方差可与状态解耦,并给出了两模型下的IMM的模型输入交互方差之间部分解耦及完全解耦的条件,从而将常增益滤波器一IMM相结合,提出两模型常增益IMM自适应滤波器算法.仿真表明在精度与IMM相当的情况下,计算量减少了约50%,并消除了单模型常增益滤波器的有偏性 |
英文摘要 |
In this paper, we firstly prove in interacting multiple modeis algorithm (IMM) model-conditional estimational variances can be decoupled with model-conditonal state estimation under certain conditions, and then supply the conditions to partly-decoupling and absolutely-decoupling among model-conditional estimational variances. Therefore IMM based on
α-β & α-β-γ filters is proposed. The Momte Carlo simulations show that this algorithm not only remains almost as accurate as IMM, but also save about half of the computation. |