| 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 950 全文点击 377 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. |